{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **1 导入检查数据**\n",
    "    * 1.1 导入数据\n",
    "    * 1.2 异常值检测\n",
    "    * 1.3 训练集与测试集合并\n",
    "    * 1.4 检查null值和缺失值  \n",
    "* **2 特征分析** \n",
    "    * 2.1 数值型\n",
    "    * 2.2 分类型值\n",
    "* **3 填充缺失值** \n",
    "    * 3.1 Age \n",
    "* **4 特征工程** \n",
    "    * 4.1 名字和称谓  \n",
    "    * 4.2 家庭人数\n",
    "    * 4.3 船舱等级\n",
    "    * 4.4 船票\n",
    "* **5 建模** \n",
    "    * 5.1 简单的模型\n",
    "    * 5.2 叠加模型 \n",
    "    * 5.3 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "\n",
    "from collections import Counter \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, \\\n",
    "                                GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, \\\n",
    "                                    cross_val_score, StratifiedKFold,\\\n",
    "                                    learning_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 加载并检查数据\n",
    "### 1.1 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "test = pd.read_csv('titanic_test.csv')\n",
    "IDtest = test['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 异常值检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 合并训练和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket  \n",
       "0       3    male      1       0.0         A/5 21171  \n",
       "1       1  female      1       1.0          PC 17599  \n",
       "2       3  female      0       1.0  STON/O2. 3101282  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_len = len(train)\n",
    "df = pd.concat(objs=[train, test], axis=0).reset_index(drop=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 检查null值和缺失值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             263\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.特征分析\n",
    "### 2.1 数值型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GpLp+7gG+o2L79mnxuLHHGHPYGPM8cAfOLn5P1PbJlVLK6yzi+VK99UBHEWkrIkE4k/KikwuJSGcgEvih3LZIEQl2PY7BOb1iVW3fHvO0H3ZXnDfNXAVk4pyM9/7aPrlSSnmbty46GmNsIjIZ+Apnt765xphtIjIV2GCMOZG8r8fZVFy+uaQr8JqIOHBWjGeU711yujxtw34LZ3V/zIlqvlJKNUTevHHGGPMF8MVJ2544af0pN8d9D/T0WiAuNSZsV1/E3caY2d5+cqWU8jaxnsG3phtj7CISLSJBxpji+ghKKaVOmx+OwucpjycwANaIyCKgdBwRY8xzdRKVUkqdJm/d6dgQeZqwU12LBWhWd+EopVQtnek1bGPM03UdiFJKecMZP1qfiHyLmzt8jDEjvR6RUkrVRs03xPgtT5tEHij3OASYgPP+eKWUalDO6F4iAMaYjSdtWiMiOkWYUqrh0SYRiSq3agH6Agl1EpFSStWCP06u6ylPm0Q2UtaGbQN+B26ri4CUUqpWztRufSLSDzhgjGnrWr8ZZ/v173hhIBOllPK6RlzDrunMXgOKAURkGPAP4B0gl6rHkFVKKZ8Rq9Xjxd/U1CRiNcZkuR5fi3M2ho+Aj0RkU92GppRSp+EM7tZnFZEAY4wNGEXFqXQ8bf/2mov6WeiYZKHEDp+usXEoq3KZFlFwxZAAAqyQfNDBl+sdFfYP7mbhwr5WnplfQsHxegq8Cme/Pp24i0dQnJ7JynMudVum26zHiBs7HHthEZtve5i8n50tUUk3jafjI3cCkPyPVzg4r9azD3nN2p+2MPvNeTgcDsaNHsFNE9yf27ffr+PxmS/wxsyn6dKhHbl5+UyZ+QI7du3hovPP475JN9dz5NW7Z1J7Bp0bTdFxO9Nn7+S33UcrlXn2qZ5ERwVhtQqbt+Xy3KvJOBxw/pAY/nRDG1q3bMKf7/+JnbsqH+sLOzav4rN3Z+Bw2Blw/gRGXvbnCvt3/7qBRfNmcGj/b9z4l5n0GnBh6b7sI6kseP1JcjIPg8DEB18lKjapvk+hsjO4l8gHwAoROQIUAqsARKQDzmaRetMxSYgOF57/1EbLGGHcACuvf2mvVG7cQCuLfrCTcsTwh1FWOiQKu1Kd10vDm0D7RCHnaFWz/NSvlHc+5veX36P33Gfc7o8dO4ywDm34rusYIgb0oseLT/H9kGsIjGxOpymTWT1wAsYYzvvxY9I+/wZbTl49n0FldruD5+a8w6ynHiIuOoqJDz7B0P59aNuq4hu5oLCQhUuW0q1T+9JtQUGBTLx+Anv3p7Bnf0p9h16tgedG0SqxCdfdvo7unZvxwJ0dmfRA5WlOH39mOwWFzv/Lvz/SjfOHxLJ8VQZ79hXw6PRtPHhXp/oOvUoOh51P3prGpEdep3l0PLOnXEu3PueT0LJDaZnImBZce8c0Vix+u9LxH7zyKKPHT6JTz8EcLzrmyQwu9aKhxFEXqj0zY8w0nBMVvA0MLTdAtwX4S92GVlGXVsKm3c7acsoRQ0iQ0DS0YpmmoRAcKKQccYa5abeDrmeVfdqO7Wdl6UZ7lZOy1bes1Rsoyar6cy/+slEcfM9Zc875cTOBzcMJTogldsxQMpavoSQ7F1tOHhnL1xB34Xn1FXa1fk3eTcsW8SQlxBEYGMDooQNZve7kbvzw+vsfccP4SwgKDCzdFhoSQq9unQkKCqxU3tfOGxjN/745DMC2nfk0DQsgOjKoUrkTydpqFQIDLJx4x+xLKeDAwYY1v+T+XVuJjm9FdHwrAgKC6D3oYrZt/LZCmajYJBLP6lzpdu/DKbtw2G106jkYgOCQMIKCT3pD+or3ZpxpcGr8KDLGrDXGfAI0E5HLRORSIM8Y81Pdh1emWRMhr6BsPa/AEN6k4h88vImQV2DKlXEeB9C5pZBfYEjLrpdwvSIkMZ7ClMOl60UHDxOSFE9IYjxFB8ptT0kjJDHeFyFWkpGVTVxMWbf92OgoMjIr/tF/2/M76UcyGdKv1lPc1ZuY6GDSj5S1oaVnHicmunLCBnj26Z4sfm8QBYU2vvs+o75CPGW52WlERLcoXY+Iiic3K82jY48c2kdoWDhvz7qH5x6ZwOf/+RcOR+VvvD4hFs8XP+NRxCIyEVgHXIlzmrC1IvKnugysUgxutlWckacKBgKtMKynhW82OWou34C4GybSGOO2n6lHf4t64C6O8ufhcDh4fu5/mHzrDfUZVq25rYtV8Se//8mtXP7HHwgMtNDn7Mi6DKt23MTv6dCkdoeNvTs2cukND3DP3+eTlX6A9SsayHUUq9Xzxc94euHwr8A5xphMABGJBr4H5rorLCKTcF2gvOSWlzn3/ImnFVz/zhb6dHR+pqRmGsKblO0LbyLkn/QN8+Rad3gTyC80RDaDiKbCnZcGlG6/fVwAry+xcbTotEKrF4UHDxPaMoET9dOQpASOp6ZTdPAwUcP7l5YLaRlP1op1vgnyJHHRUaQfKbsanJGZRUxUROl6QWERe/en8Jcp0wHIysnloemzeObRe+nSoV29x1udKy9O5NILnTXQX5PziYsJLt0XFx3Mkayq5/MoLjGs/jGT8wZEs2FTw/xa1zwqnpzMQ6XrOVlphEfGeXRsRFQCiW26Eh3vnFS8R99R7Nu1GedtGj7mhzVnT3l6ZilAfrn1fOBAVYWNMXOMMX2NMX1PN1kDrNvp4NXFNl5dbOPX/Q56t3eG2zJGKCoxHD0pYR8tdL5RWsY4k3bv9hZ2HDCk58DMBTb+/bFzySuA1xY37GQNkP75NyT9YTwAEQN6YcvL5/jhDDKWriZ29FACIsIJiAgndvRQMpau9nG0Tl06tuPAocOkpqVTUmLj69VrGdKvT+n+pmFNWPLuKyycM4uFc2bRrVP7BpmsAT7+IpVb79nIrfdsZNXaI4wd6RyNoXvnZhwtsJGZXTFhh4ZYStu1rRYY1DeKfSkFlX5vQ9GqfQ+OHN5PZnoKNlsxm374gu7nnu/xsYXHcjma5/xwTt72I/FJ7Ws4qp404jZsT2vYB4EfReQznF+kLgfWich9UD8zzyQfNHRKMtxzRQAlNvj0+7L2sjvGBfDqYufggYt/dDB+sJXAAGe3vuSDDaOpwJ3e854lenh/gmIiGbl3BclTX0ACnS/J/jkfkv7lCmIvGs6IHcuwFxayZeKjAJRk55I8/WWG/rAQgORpL1GSXa+ddqoUYLVy35//yH1Pz8ThcHDJqGG0O6slb7z/EV06tGVo/z7VHn/VpHs5VliIzWZj1bqNPPfkQ5V6mPjCDxuyGNQ3ivlz+pd26zvhrdnncus9GwkJsTLj8e4EBliwWoWNm3P47EvnnNXDBkbz/27vSETzQGY+0ZPkvUe5/8mtvjodAKzWAK645TFenzEJ43DQb8QVJLTswP8WvECrdt3pfu5I9u/eyjuz7qHgWB7bf/qOpQtf4q8zF2GxWLn0xr/y2rTbMBhatu3GgJFX+fR8SjXiGrZ40vYpIk9Wt7+6CQ6efLek4WbM09T/th6+DqFO9N88z9cheN0VDzWsnhne8tBTQ3wdQp249NyAWld7ixa95HHOCbnsLr+qZp/yjDMiEgnkmIZylUsppco7U8cSEZEnRKSL63GwiHwD7AbSRGR0fQSolFKnxGL1fPEzNX0UXQucaKy72VU+FhgOTK/DuJRS6vRYLJ4vfqamJpHick0fFwIfGGPswK8iUu9jiSilVI0a8XjYNX3EHBeRHiISC5wPLC23r0kVxyillO804jsda6ol3wMsxNkMMssYsxdARC4GKo98o5RSvtaIa9jVJmxjzI9AFzfbvwC+qKuglFLqdBk/vOXcU56OJRItIs+LyE8islFEZrtuT1dKqYalETeJeBrxh0AGzoECrnI9nl9XQSml1GlrxAnb054eUcaYv5Vb/7uIjK+LgJRSqjZMI27D9vQj5lsRuU5ELK7lGmBJXQamlFKnxYs1bBEZKyI7RWSXiDzsZv8tIpIhIptcy8Ry+24WkWTX4pX57qqtYYtIPs7BngS4Dzgx2IQVOApUO8aIUkrVOy/VsEXECrwEXIBzxNL1IrLIGLP9pKLzjTGTTzo2Cmd+7Iszh250HVursXZr6iXSrDa/XCml6psXe4n0B3YZY/YAiMiHOEcqPTlhu3MhsMwYk+U6dhkwFuc8uaetphp2F2PMDhFxOyZmfU8TppRSNTqFi4nlJ1txmWOMmeN6nETFcf9TgAFufs0EERkG/Abca4w5UMWxtR4nuKaLjvfhPJlny20rP0rfyNoGoJRS3mROIWG7kvOcKna7nZnwpPXPcQ7ZcVxE7gDewZkXPTn2lNV0Zm+ISIIx5nxjzPk4Z08/CvyCs3ufUko1LCKeL9VLAVqVW28JpJYvYIzJNMacmJ35deBcT489HTUl7FeBYgBXlf8fOD9Bcqn6U0kppXzGiMXjpQbrgY4i0lZEgoDrgEXlC4hIi3KrlwG/uh5/BYwRkUjXHAJjXNtqpaYmEeuJRnOcQ63OMcZ8BHwkIps8eYI2rQJrE1+D1BhnZgFY1+smX4fgdUn3LK25kB8KD6l6AmD/5oVBQL00zrUxxiYik3EmWisw1xizTUSmAhuMMYuAu0XkMsAGZAG3uI7NEpG/4Uz6AFPL5dLTVmPCFpEAY4wNGEXFxnkdXlUp1eB488YZd+MmGWOeKPf4EeCRKo6dC8z1WjDUnHQ/AFaIyBGgEFgFICIdcDaLKKVUw+KHt5x7qqZ+2NNEZDnQAlhabjIDC/CXug5OKaVOlXHbQaNxqLFZwxiz1s223+omHKWUqp1T6dbnb7QdWinVuGjCVkop/+Dww9nQPaUJWynVuDTi4VU1YSulGhVtw1ZKKT9xRvcSUUopf6I1bKWU8hfahq2UUv7BIdpLRCml/II2iSillJ/Qi45KKeUntIatlFJ+wpvDqzY0mrCVUo2KXnRsAPZsW8nX/52Gw+Gg15CrGTR2UoX9tpJiFr/9IIf3byM0LILLJ84iIqYldnsJX86bQtr+7TgcNnoMHM+gsbf76CwqWvvTFma/OQ+Hw8G40SO4acKlbst9+/06Hp/5Am/MfJouHdqRm5fPlJkvsGPXHi46/zzum3RzPUdetbNfn07cxSMoTs9k5Tnuz6fbrMeIGzsce2ERm297mLyftwOQdNN4Oj5yJwDJ/3iFg/M+rbe4PXHL5ZGc0zWU48WGV+Znsvdg5Vlfrh0bwbC+YTQNtXDzY2WTZsdEWrnjmmjCw6wcLXTw4vtHyMq112f4bv3y0xrmz52Jw+Fg6OjxXHTlnyrsX7ZoHqu//gSLNYBm4ZHcfNeTRMclcmDvTv7z2jQKC49hsVi5eMJt9Bt6oY/OoqLG3IbtF409DoedpR9M5ZrJb/DnJ5ewff1ijqTuqlBmy5oFhDQJ546/LaPfqFv47pN/AbBj4/+w24q57YnPueXRj/l55XxyjqT44CwqstsdPDfnHf71+F957/ln+Hr1D+w9cLBSuYLCQhYuWUq3Tu1LtwUFBTLx+gncdfP19RmyR1Le+Zh14yZWuT927DDCOrThu65j2Hrn4/R48SkAAiOb02nKZNYMuYbVg6+m05TJBESE11PUNevdJYSE2EDumZHK6wszuW1ClNtyP20v4LHZhyttv2lcJCs3HuPB5w7x0bJcrr84oq5DrpHDbuf912dw95QXeXr2R6xf9T9SD+yuUKZV2y48OvM/PDnrv/QZNIqP3p0NQFBwCLfe/Teenv0R9zz+IvPn/ouCY/m+OI1KvDinY4PjFxEf+n0LkXGtiYhthTUgiG79LiF5y/IKZZK3fEPPQVcA0KXPhezb8QPGGESE4uOFOOw2bMVFWAMCCQ5t6ovTqODX5N20bBFPUkIcgYEBjB46kNXrNlYq9/r7H3HD+EsICiybGzM0JIRe3ToTFNTw5svMWr2BkqyqJyOKv2wUB99z1pxzftxMYPNwghNiiR0zlIzlayjJzsWWk0fG8jXEXXhefYVdo37dm7Byw1EAkvcXExZiIaJZ5a/eyfuLycmvXHNOig/kl+QiALbtKqJv9yZ1G7AH9u76hbgWrYhNaElAYCD9hl7I5nXfVSjTpWc/goNDAWjX6WyyM9MAiE9sTXxiawAiouIIbx5Jfm6tpyz0CoN4vPg3LF/+AAAgAElEQVQbjxO2iCSJyGARGXZiqcvAysvPTqNZZELperOIePKz0yqWyUmjWaRzAmOLNYDg0GYUHsumc58LCQoO5YWHhvLyo+cz4II/ERrm+9pNRlY2cTFltbTY6CgyMrMrlPltz++kH8lkSL9z6ju8OhOSGE9hSlkNtOjgYUKS4glJjKfoQLntKWmEJMb7IkS3IptbycwpS8SZuTaimnveVrovtYQBPZ1Jun+PUJqEWGjaxLf1pZzMdKKiy/7GEdHxZGdlVFl+9fJP6dFnSKXte5N/wWazEZvQqk7iPFVnfA1bRJ4B1gBTgL+6lgeqKT9JRDaIyIbvFs/xQpim8qaTrwSbymUE4dDeLYhYmPzMKu74+3LWfT2XnIwDlcrWN+Mu3nLn5HA4eH7uf5h86w31GVadEzdX8I0xbm8ndvc38hV3HQ9OJbz3FmfTrX0IM+5tQdf2IWTm2LA7fHt+7p69qjrn2hVL2LdrO2PGV7xekpOVwdzZU7hl8lNYLA0jATbmGranFx3HA52NMcc9KWyMmQPMAXjrW7f/F6ekWWQC+dllta/8nDSaRcS5KXOI8MgEHHYbxwvzCQmLYPv6xbTrfh5WayBh4dEkte/DoX1biYj1bW0gLjqK9CNlXyEzMrOIiSqr+RcUFrF3fwp/mTIdgKycXB6aPotnHr2XLh3a1Xu83lJ48DChLRM48V0iJCmB46npFB08TNTw/qXlQlrGk7VinW+CdBkzuCmjBjQDYPeB40RHlNWoo5sHkJ3n+UXD7Dw7z77jrL0GBwkDejahsMi3CTsyOo6szLJvqjmZaURExVYqt33zWr5Y+CYP/O0NAgODSrcXFhzlhWl3c/kNd9Gu89n1ErMnHP7R0ntaPD2zPYDPGkxbtO5JVvrv5Bw5gN1WzPb1S+hw9sgKZTqcPZKtP3wCwI6fvqJ154GICOFRLdi380eMMRQfLyB1z2aiE3yf8Lp0bMeBQ4dJTUunpMTG16vXMqRfn9L9TcOasOTdV1g4ZxYL58yiW6f2fp+sAdI//4akP4wHIGJAL2x5+Rw/nEHG0tXEjh5KQEQ4ARHhxI4eSsbS1T6Nden3R3lo1iEemnWI9dsKGdbXee2j41lBFBQ53LZVV6VZE0tpLX38yOZ8u/5oXYR8Stp06E76of0cSTuIraSE9au/ole/ERXK7N+zg/dencZdj8wiPKKsCc9WUsIrz9zPoBHj6Dv4gnqOvHoGi8eLv6m2hi0iL+D85lQAbHLNoF5ayzbG3F234TlZrAGMufYJ5j8/EeOwc/bgCcQmdmTlotm0aN2Djr1G0WvIVXz+1l959fELCG3SnMsnzgKgz/AbWfLuI7w5dRzGGM4efCVxLbvUR9jVCrBaue/Pf+S+p51dqi4ZNYx2Z7Xkjfc/okuHtgzt36fa46+adC/HCgux2WysWreR5558iLatkuop+qr1nvcs0cP7ExQTyci9K0ie+gIS6Pw32z/nQ9K/XEHsRcMZsWMZ9sJCtkx8FICS7FySp7/M0B8WApA87SVKsqu+eFnffv61kHO6hDL74USKS5zd+k545t4WPDTrEAA3XhLBkHPCCAoUXp6SxDfrjrJwaS7dOoRw/UURGGDHniLe/Nj3F+is1gCun/gQ/576fzgcDoaMupzEs9rz2Qcv07p9N3r3H8HCd2dxvKiA1/71IABRMQlMfnQ2G75fym/bf+Jofg7ff7sIgFv/MpVWbTv78pSAxt2tT6prJxSRajv4GmPeqekJvNEk0tCMi/ftV/W6sq7XTb4OwevevWepr0OoE/93a+Wmi8ZgePcmtc62O3cf8DjndG7fyq+ye7U17BMJWUTCgCJjjN21bgWC6z48pZQ6NY25hu1pI85yILTceijwtffDUUqp2tFeIhBijCm9SmKMOSoivu/5r5RSJ3EY/7uY6ClPz+yYiJReBRORc4HCuglJKaVOn9aw4R5ggYikutZbANfWTUhKKXX6/DERe6rGhC0iFiAI6AJ0xnkz1A5jTEkdx6aUUqfMmDM4YRtjHCLyrDFmEPBLPcSklFKnzdGIa9ietmEvFZEJ4m4gCKWUakAcxuLx4m88jfg+YAFwXETyRCRfRPLqMC6llDot3rzoKCJjRWSniOwSkYfd7L9PRLaLyBYRWS4ircvts4vIJteyyBvn5tFFR2NMM288mVJK1TVvtWG7bhB8CbgASAHWi8giY8z2csV+BvoaYwpE5E7gn5R1yCg0xvT2SjAuHk8RJiKRQEcg5MQ2Y8xKbwajlFK15cVeIv2BXcaYPQAi8iFwOVCasI0x35Yrvxb4g7ee3B1Px8OeCKwEvgKedv18qu7CUkqp02OMeLyUH7vftZSfLDYJKD94foprW1VuA74stx7i+p1rRWS8N87tVPph9wPWGmPOF5EuOBO3Uko1KI5TKFt+7H433FXV3Q4sJSJ/APoCw8ttPssYkyoi7YBvRGSrMWa3u+M95WnCLjLGFIkIIhJsjNkhIr4fR1EppU7ixd4fKUD5mU5aAqknFxKR0cBjwPDyk7wYY1JdP/eIyHfAOUCtEranZ5YiIhHAp8AyEfnMXeBKKeVrp9IkUoP1QEcRaSsiQcB1QIXeHiJyDvAacJkxJr3c9kgRCXY9jgGGUK7t+3R52kvkCtfDp0TkW6A58L/aPrlSSnmbty46GmNsIjIZ5zU7KzDXGLNNRKYCG4wxi4CZQFOcQ3cA7DfGXAZ0BV4TEQfOivGMk3qXnJaaJjAIAe4AOgBbgTeNMbZTeYIHXilodBMYrP1iva9DqBNJndv4OgSv++PsMb4OoU5kLd/p6xDqxE3Dap9tV2475nHOGdY9zK9uBqyphv0OUAKsAi4CuuG8AKmUUg3SmTz4UzdjTE8AEXkTaJxzYymlGo0zefCn0hH5XO05dRyOUkrVjv0MTti9yo0ZIkCoa10AY4wJr9PolFLqFJ2xNWxjjLW+AlFKKW+oph+F3/N4LBGllPIHZ/JFR6WU8isOrWErpZR/cDi0hq2UUn6hMU8RpglbKdWo6EVHpZTyE2dstz6llPI3etFRKaX8hDaJKKWUnziTb01XSim/ojVspZTyE5qwlVLKTzi0SaRhuHxIIF1bWym2wfxvjnPwSOWP0qQY4bqRwQQGwK/77Hy2xjlCbGK0MGF4EAFWweEwfLyqhAPppzK/ct24Z1J7Bp0bTdFxO9Nn7+S33UcrlXn2qZ5ERwVhtQqbt+Xy3KvJOBxw/pAY/nRDG1q3bMKf7/+JnbsqH+srt1weyTldQzlebHhlfiZ7DxZXKnPt2AiG9Q2jaaiFmx87ULo9JtLKHddEEx5m5WihgxffP0JWrr0+w6/k7NenE3fxCIrTM1l5zqVuy3Sb9RhxY4djLyxi820Pk/ezc0aopJvG0/GROwFI/scrHJz3ab3FXZPdv6zkqw+nYRwOep93NUMumlRh/77f1rNs/nTSUnZy5aTn6Hru2NJ9m7//hNVLXgFg6CV30mvwFTQEjbmG7bXphetal7MsxEZYmPF+EQtXFDNhWJDbchOGBbFwRTEz3i8iNsJCl7Ocp3jJoCCWbShh1oIivlpfwriBgfUZvlsDz42iVWITrrt9HTNf+o0H7uzottzjz2znlrs3ctNdG4hoHsj5Q2IB2LOvgEenb2Pzttz6DLtGvbuEkBAbyD0zUnl9YSa3TYhyW+6n7QU8Nvtwpe03jYtk5cZjPPjcIT5alsv1F0fUdcg1SnnnY9aNm1jl/tixwwjr0Ibvuo5h652P0+PFpwAIjGxOpymTWTPkGlYPvppOUyYTENEwRiV2OOx8+f5Urr/nDe6YuoRt6xaTkbqrQpnmUS249NZ/0KP/uArbC4/lsOrzF/nTo//lT48uYNXnL1J4rGH8H9odni/+xm8Sdvc2VjbsdE4nuT/NQUiw0KxJxTLNmkBIkLAvzflKbNhpo3sb1wixBoIDnV+VQoKE3AYw1eR5A6P53zfOhLVtZz5NwwKIjqz8QVRQ6KxdWq1CYICltAaxL6WAAwcL6y1eT/Xr3oSVG5y1/eT9xYSFWIhoVnmk3uT9xeTkV645J8UH8ktyEQDbdhXRt3uTSmXqW9bqDZRkVZ2Q4i8bxcH3nDXnnB83E9g8nOCEWGLHDCVj+RpKsnOx5eSRsXwNcReeV19hVyt17xaiYlsTGdsKa0AQ3ftdwm+bllcoExHTkviWXRCpmCp2/7Katt2GEBoWQWhYc9p2G8LuX1bVZ/hV8uKs6Q2O3yTs5mEWco6WJdnco4bmYZbKZcrNv5l7rKzMZ2uKGTcokCk3hXDpoEC+XFuCr8VEB5N+5HjpenrmcWKi3X9zePbpnix+bxAFhTa++z6jvkI8LZHNrWTmlCXizFwbUc09H1p9X2oJA3o6k3T/HqE0CbHQtEnD/lcNSYynMKXs20LRwcOEJMUTkhhP0YFy21PSCEmM90WIleTnpBEelVC63iwynvycNM+PjSw7NvwUjq1rxni++Jsa3wUiEi8ib4rIl671biJyW92HdnIclbedPON7dZ+Xg7oHsOj7Ev4+r4hF35dw9fnuE2N9chtvFf9E9z+5lcv/+AOBgRb6nB1Zl2HVmvvXyvPj31ucTbf2Icy4twVd24eQmWPD3sBvX3M3fZ4xxu0f4+T/W19xH4entc7KxzaUKQQdxvPF33hy0fFt4C3gMdf6b8B84M2qDhCRScAkgAtueIGzh/7ptIIb3D2AAd2cIR5IdxDRtOwfonlTIe+kZo2cYw4iys1a3zxMyD3mbB7p2zmAz9Y4mw8277Zz9QjfJOwrL07k0gtbAPBrcj5xMcGl++KigzmSVfni3AnFJYbVP2Zy3oBoNmzKrvNYT8WYwU0ZNaAZALsPHCc6oqxGHd08gOw8zy8aZufZefYd57eI4CBhQM8mFBY17HdX4cHDhLZM4MSrEpKUwPHUdIoOHiZqeP/SciEt48la0TDmsg6PTCAvq6z2n5+dRrOIOI+ObRaRwL7fys4jLzuN1p36V3NE/Wkgn4d1wpPvmTHGmP8CDnBOxgtU++4zxswxxvQ1xvQ93WQN8P02G7MWFDFrQRHb9tro29mZvM+Kt1B03JBfULF8fgEcLzGcFe88rb6dA9j2uzPUvAJD+0Tn9g5JFo7k+uZV/fiLVG69ZyO33rORVWuPMHak82tl987NOFpgIzO7YsIODbGUtmtbLTCobxT7Ugoq/V5fW/r9UR6adYiHZh1i/bZChvVtCkDHs4IoKHK4bauuSrMmltKK6fiRzfl2fcPp/VKV9M+/IekP4wGIGNALW14+xw9nkLF0NbGjhxIQEU5ARDixo4eSsXS1j6N1SmzTk6z038nOOIDdVsy29Uvo1GukR8e27zGUPdtWU3gsl8JjuezZtpr2PYbWccSeacxNIp7UsI+JSDSu70AiMhCo98vBv+530KW1g4dvCKHEBvO/LUts914dwqwFzotUH60s4bqRQQRYYed+Ozv2O2vYC74rZvzQICwCNrthwXfH3T5PffphQxaD+kYxf07/0m59J7w1+1xuvWcjISFWZjzencAAC1arsHFzDp99mQrAsIHR/L/bOxLRPJCZT/Qkee9R7n9yq69Op9TPvxZyTpdQZj+cSHGJs1vfCc/c24KHZh0C4MZLIhhyThhBgcLLU5L4Zt1RFi7NpVuHEK6/KAID7NhTxJsfZ/noTMr0nvcs0cP7ExQTyci9K0ie+gIS6Hz77J/zIelfriD2ouGM2LEMe2EhWyY+CkBJdi7J019m6A8LAUie9hIl2Q2jN4XFGsDYG57gg39PxGHs9B4ygdikjnz32WwSW/egU+9RpO7dwoKXJ1NUkEfylm9Z8dkL3DF1CaFhEZw37v+YO+0qAM679C5Cw3zfmwf8s/eHp6Sm9jQR6QO8APQAfgFigauMMVs8eYIHXmkA3TG8bO0X630dQp1I6tzG1yF43R9nj/F1CHUia/nOmgv5oZuG1X72gdeWVnUlqLLbx/jXbAc11rCNMT+JyHCgM84rEjuNMb7vYqGUUm74Y1OHp2pM2CJy5UmbOolILrDVGJNeN2EppdTpOaMTNnAbMAj41rU+AliLM3FPNcbMq6PYlFLqlPljdz1PeZKwHUBXY0waOPtlA68AA4CVgCZspVSDcWr93P2qCdujhN3mRLJ2SQc6GWOyRETbspVSDYrdt+OE1SlPEvYqEVkMLHCtTwBWikgYkFNnkSml1GlozG3Yntw4cxfOOx17u5Z1gDHGHDPGnF+XwSml1Kny5q3pIjJWRHaKyC4RedjN/mARme/a/6OItCm37xHX9p0icqE3zq3GhG2cDUK7gRLgCmAU8Ks3nlwppbzNW3c6iogVeAm4COgGXC8i3U4qdhuQbYzpAMwCnnEd2w24DugOjAVedv2+WqmySUREOrme8HogE+f4IaK1aqVUQ2ZOqZtItRcd+wO7jDF7AETkQ+ByYHu5MpcDT7keLwReFOcoWJcDHxpjjgN7RWSX6/f9cArBVVJdG/YOYBVwqTFmlyvge2vzZEopVde8eGt6EnCg3HoKzt5xbssYY2yue1SiXdvXnnRsUm0Dqq5JZAJwGPhWRF4XkVH4Wx8YpdQZx+EwHi8iMklENpRbys+R5i7fnVx9r6qMJ8eesipr2MaYT4BPXL1BxgP3AvEi8grwiTFmaW2fXCmlvO1UeokYY+YAc6rYnQK0KrfeEkitokyKiAQAzYEsD489ZZ5cdDxmjPmPMWac60k3AZWuliqlVEPgxeFV1wMdRaStiAThvKa36KQyi4CbXY+vAr5xddRYBFzn6kXSFuiIs4ddrZzSrOnGmCzgNdeilFINjsNLHbFdbdKTga8AKzDXGLNNRKYCG4wxi3BO5DLPdVExC2dSx1XuvzgvUNqAu4wxtb6l55QStlJKNXTGi+NhG2O+AL44adsT5R4XAVdXcew0YJr3otGErZRqZOz2xnuroyZspVSj0lAmOa4LdZ6wrx1Z9aSy/mp4/yG+DqFOhIc0vtdq/2WNc2aWqFGdfR1C3Sip/et1pg+vqpRSfuPU7nT0L5qwlVKNSiNuEdGErZRqXBxaw1ZKKf/g0F4iSinlH7x140xDpAlbKdWoaLc+pZTyE9qGrZRSfqIRV7A1YSulGhe7F2cwaGg0YSulGhW9cUYppfyEJmyllPITjThfa8JWSjUuWsNWSik/of2wlVLKT2gvEaWU8hPaJKKUUn5CE7aPbd74A/PeeA6H3cGIMZdx2VU3V9j/xafv892yz7BaAmjWPIJJd08hJq4FAEcyDvPGC9PIOpIOAn99Yhax8Ym+OI1KdmxexWfvzsDhsDPg/AmMvOzPFfbv/nUDi+bN4ND+37jxLzPpNeDC0n3ZR1JZ8PqT5GQeBoGJD75KVGxSfZ+CW7/8tIb5c2ficDgYOno8F135pwr7ly2ax+qvP8FiDaBZeCQ33/Uk0XGJHNi7k/+8No3CwmNYLFYunnAb/YZeWMWz1K/dv6zkqw+nYRwOep93NUMumlRh/77f1rNs/nTSUnZy5aTn6Hru2NJ9m7//hNVLXgFg6CV30mvwFfUae1XOfn06cRePoDg9k5XnXOq2TLdZjxE3djj2wiI23/YweT9vByDppvF0fOROAJL/8QoH531ab3HXRAd/8iGH3c47r83k4akvEBUdxxP338K5/c8j6ax2pWXatOvE3557h+DgEL7+4iM+ePtF/vKgc7LiV2c9zeVX30LPcwZQVFiAWCy+OpUKHA47n7w1jUmPvE7z6HhmT7mWbn3OJ6Flh9IykTEtuPaOaaxY/Hal4z945VFGj59Ep56DOV50DJEGcl52O++/PoN7n3yFyOh4pj94I736DSexVfvSMq3aduHRmf8hODiU7/73Xz56dzaTHniGoOAQbr37b8QntiYnK52/P3Aj3c8ZTJOwZj48I+dr9eX7U7nx3rcIj4znzWlX0anXSGITy16r5lEtuPTWf7D2q7kVji08lsOqz1/ktikfAcKbf7+STr1GEhrWvJ7PorKUdz7m95ffo/fcZ9zujx07jLAObfiu6xgiBvSix4tP8f2QawiMbE6nKZNZPXACxhjO+/Fj0j7/BltOXj2fgXuNuYbdMN7l1didvJ34Fi2JS0giIDCQgeddwMYfV1Yo0+3svgQHhwDQoXMPZ20aOLh/Dw67jZ7nDAAgJLRJaTlf279rK9HxrYiOb0VAQBC9B13Mto3fVigTFZtE4lmdEYtU2H44ZRcOu41OPQcDEBwSRlBwaL3FXp29u34hrkUrYhNaEhAYSL+hF7J53XcVynTp2Y9gV7ztOp1NdmYaAPGJrYlPbA1ARFQc4c0jyc/Nqtf43Undu4Wo2NZExrbCGhBE936X8Num5RXKRMS0JL5ll0ofnLt/WU3bbkMIDYsgNKw5bbsNYfcvq+oz/Cplrd5ASVZulfvjLxvFwfecNeecHzcT2Dyc4IRYYscMJWP5Gkqyc7Hl5JGxfA1xF55XX2HXyBjj8eJvPKphi4gANwLtjDFTReQsIMEYs65OowOyM9OJiokvXY+KiWP3zm1Vll+xbBG9zh0EwKHUAzQJa8a/pz9ERloq3Xv347o/3oXFaq3rsGuUm51GRHSL0vWIqHj27dri0bFHDu0jNCyct2fdQ1Z6Ch17DOKS6+/FYvH9eeVkphMVXfZ6RUTHszf5lyrLr17+KT36VJ7UeG/yL9hsNmITWtVJnKciPyeN8KiE0vVmkfGk7vXstcrPSSM8suzY8Mh48nPSvB5jXQhJjKcw5XDpetHBw4QkxROSGE/RgXLbU9IISYx39yt8wm5rvL1EPK1hvwwMAq53recDL1VVWEQmicgGEdnwyfy3axWg2w9BETcbYfW3X7Jn169ccuUfAHDYbezcvokb/nQ3U597i4zDB1m5fEmt4vEaN+clVZzXyewOG3t3bOTSGx7gnr/PJyv9AOtXNIw2RLcvVxVl165Ywr5d2xkzvuI1iZysDObOnsItk5/C0gCasNzXxDx7rdz9RTx9nX3NXZzGGLfvv4ZUW23MNWxP3w0DjDF3AUUAxphsIKiqwsaYOcaYvsaYvldce0utAoyKiSPrSFmNJOtIOpFRMZXK/bJpHYsWvM19U/5FYKAztKjoOFq360xcQhJWawDnDhzO73t21Coeb2keFU9O5qHS9ZysNMIj4zw6NiIqgcQ2XYmOb4XVGkCPvqM4+Pv2ugr1lERGx5GVWfZ65WSmEREVW6nc9s1r+WLhm9z1yL9LXy+AwoKjvDDtbi6/4S7adT67XmKuSXhkAnlZZTXK/Ow0mkV49lo1i0ggL7vs2LzsNJo29+xYXys8eJjQlmXfDkKSEjiemu6sabcqt71lPMcPpfsiRLeMw+Hx4m88TdglImLFVV0QkVigXs62XceuHE49QPrhVGwlJaxdtYw+A4ZVKPP77p3MfXkG902ZSfOIqHLHdqPgaB55udkAbNuygaRWbesj7Bq1at+DI4f3k5megs1WzKYfvqD7ued7fGzhsVyO5jnbd5O3/Uh8UvsajqofbTp0J/3Qfo6kHcRWUsL61V/Rq9+ICmX279nBe69O465HZhFe7vWylZTwyjP3M2jEOPoOvqCeI69aYpueZKX/TnbGAey2YratX0KnXiM9OrZ9j6Hs2baawmO5FB7LZc+21bTvMbSOI/aO9M+/IekP4wGIGNALW14+xw9nkLF0NbGjhxIQEU5ARDixo4eSsXS1j6Mt43AYjxd/I558LRCRG4FrgT7AO8BVwBRjzIKajl2/M6fWf5VNG9bw3huzcDgcDB99KZdfcysL//MabTt05dwBw/jH45M58PsuIlw17+jYBO6f8i8Atv78I+/PfR6DoW37Ltx21yMEBAbWKp7DR5vW9pQA+PXnlXw2bwbG4aDfiCsYPf52/rfgBVq16073c0eyf/dW3pl1DwXH8ggMDKJZ8xj+OnMRAL9t/Z7P35uJwdCybTeumvgUAQFVfunxSHhIsRfOCrZuXMX8uf/C4XAwZNTlXHLVRD774GVat+9G7/4jeO6p2zm4bxfNI52vV1RMApMfnc3aFUt4+8WnSGxV1gPo1r9MpVXbzqcdy/7MJrU+H4BdW1ew9MPpOIyd3kMmMPSSO/nus9kktu5Bp96jSN27hQUvT6aoII+AwGDCwmO4Y6qz+W3T6oWs+eI1AIZccge9h0yodTxRo07/b3JC73nPEj28P0ExkRxPyyR56gtIoPOy1v45HwLQ/fkniB1zHvbCQrZMfJTcjc7rES1vmUCHh24HYNeMV0l55+NaxwNwScnOWrcXXXP/7x7nnP8+28Y/2qdcPErYACLSBRiFs/FuuTHmV0+O80bCbmi8lbAbGm8l7IbEWwm7ofFGwm6IvJGwr7pnj8c5Z+Hsdn6VsGvsJSLOfkpbjDE9gIbRAKyUUlVwGP9rm/ZUjQnbGOMQkc0icpYxZn99BKWUUqerMd844+mdji2AbSKyDjh2YqMx5rI6iUoppU6TJmx4uk6jUEopL/HH/tWe8ihhG2NW1HUgSinlDY566l8tIlHAfKAN8DtwjeselfJlegOvAOGAHZhmjJnv2vc2MBw4MT7ALcaYTdU9p0f9sEVkoIisF5GjIlIsInYRaRgjvSilVDkOu93jpZYextljriOw3LV+sgLgj8aY7sBY4N8iElFu/1+NMb1dS7XJGjy/ceZFnLelJwOhwETXNqWUalCMw3i81NLlOO9LwfVzfKVYjPnNGJPsepwKpAOVb/31kMcDNRhjdgFWY4zdGPMWMOJ0n1QpperKqSTs8uMeuZZJNT9DqXhjzCEA189qxxwQkf44h/TYXW7zNBHZIiKzRCS4pif09KJjgYgEAZtE5J/AISDMw2OVUqrenEo/bGPMHGBOVftF5Gsgwc2ux04lJhFpAcwDbjamNMBHgMM4k/gc4CFganW/x9OEfRPO2vhk4F6gFVD7+2uVUsrLvNmtzxgzuqp9IpImIi2MMYdcCdntCFgiEg4swTmcx/R6XvIAAAeiSURBVNpyv/vE6G/HReQt4IGa4qk2YZ+4WcYYs8+1qQjt4qeUasDqcRS+RcDNwAzXz89OLuBqmfgEePfksZfKJXvB2f5d9cDxLjW1YZcOsiwiH9UYvlJK+Vg99hKZAVwgIsnABa51RKSviLzhKnMNMAy4RUQ2uZbern3/EZGtwFYgBvh7TU9YU5NI+YFR2lVZSimlGoj6GjbVGJOJc0C8k7dvwNmTDmPMe8B7VRzv2Ri95dSUsE0Vj5VSqkHyx4kJPFVTwu7lukFGgNByN8sIYIwx4XUanVJKnaIzdiwRY4zvZ3VVSqlTYM7k4VWVUsqfOGy1vpjYYHk844w/EJFJro7wjUpjPK/GeE7QOM+rMZ6Tv/L41nQ/cSq3lfqTxnhejfGcoHGeV2M8J7/U2BK2Uko1WpqwlVLKTzS2hN1Y29ka43k1xnOCxnlejfGc/FKjuuiolPr/7Z1djFVXFYC/DxgDhDoGaLUkBBpFamlsU8BKiZGq8UVj0zj+IG1KUk01TTR9MTGaSuHBNFFjYzOm2pog0ZTiXxGbWgKModYC0gwqaNsomGqtkaY/GaSY0uXDXpe5gTvjDJ2ZOxf2l5zM2ufsve9aZ5+z7t7rzD2rci5zrs2wK5VK5ZylOuxKpVLpENrqsNUvqwcz40K/evUY9PkRtVVutbPpa2As+jmtzzNsVu9VLxvuMzOv5p5s8yd13VjrdrZkjs9+9Y/qFnXmGPS5Vp10aejU69VQL223Lq+XpnFrbAvbrVNleNr2S0d1BfBh4KqIOKHOpWReGEnbaRHxaqtjEbGV8p7aScdQNkfEp0fQfCMlK/MBdSqweDx1HSXHI+JKAPWHwGeBb46koTo1Ijrpp2mrgUeBTwLr2qvK6+bUuI2GDhyzc4Z2zrAvBo5GxAmAiDgaEc+qR9KRNd4r25fyOvW76iPAD3K2uaTRmdqnLm3MzNTu7GtKHp+pPqN2qW9VH1b3q7sbsyX1EvW3mSF+wwTa3Kcua7LlG+oT6g61kbDzIkpqNjKv5qGm87JJ3ak+rX5mHPQeDbuBt6VuP89zfLA5V546oK5X9wAr1OXqY+oBda96QVadl+P0dKamayvqLGAlcDPFYaNOUXvTxm3qQ2pPHluq/jrPwa8sWUkmNerCvCeeyO2a3L9K3aX+iPL+ZtQbcrz61XtyIlEZTyKiLRswC+gHngJ6gffm/iPA3JSXAX0prwP2AzOyfBtwR8oXA0+lvBa4O+UHgWtT/gRwb8o7gEUpXw3sTHkrJSU9wK3AwATZ3AcsSzmANSnf3mTL7cALlOwVtwDTm87LAUo2+7nAM8C8CR7Lgfw7Lc/557I8O//OoGTTmNNk48dTfgPwV2B5lt+Y/azN/d3AdOBvwPx2Xa+p2w3AfSk/BlwF9AAPUSY/b8kx6gG6ss6FTdff99upfwt7Tub12A/8LPfNbLq2FgG/S3kVcAy4JMvvAH4BdGW5t3Hv1G38traFRCJiQF0KvAe4Ftg8gtjz1og4nvIDwHbgq5SsDlta1N9MuVF2UWZEvTlLugbYoqfyMzSyFa9kMFflJuDO0do1HCO0+bXUG8qLz3+abddnuOGDwKcoS/NVWe/BPC/H1V3Au2jKFjQBzFD7U94N3Jfy59XrU55PcQDPUxxFI4PRYuCfEbEPICJeBsix2RERL2X5ELCA8oXULlYD30r5/ix3AVuivCLuuTz/UOy6HNietkwlV0iTiFYhkS7gbktWlJPA25uO7Y2Iwym/H1gK7Ev7ZjBETsPK2NHWt/VFiYP1AX2WVDk3Aa8yGKqZflqTY01t/6E+r76T4pRvafERW4GvqbMpF9dOSrb3F1tcqKe6PktzRsQQNg/bpKntX4DvqN8D/q3OOb3OEOXx5owbX10FfABYERH/ydBWYzxficEYqAyt74km+STtfeYyB3gfcLkaFAcclBVPyybAwYhYMUEqjhW3Af8CrqDch680HTvWJAtsjIgvTaBu5z1ti2Gri9VFTbuupCx7j1CcK/z/zOz3A18EuiPiD6cfjIgBYC9wF7AtSuz3ZeCw+rHUQ/WKbPIbMjYJrBm9VcMzjM3NTKEsqaHMpB/Nth9ycEmwiOLAXszyder0dCqrgH1jrftZ0A28kM76UuDdQ9T7MyVWvRxAvUCdjK/97aEkUl0QEQsjYj5wGDgKfDRj2W9mcNXzJHCh5UEz+exkSauOJxndlBXPa8CNlC+mVuwAetSLANTZ6oIJ0vG8pZ0PHWcBG9VD6u+Byyjx2DuAu9TdFKc0HD+mONgHhqmzmRJ73Ny0bw1ws3oAOAhcl/u/ANyq7qNcuGPNUDY3cwxYou6nzOjW5/4bgScz9LCJEudunJ+9wC+Bx4ENEfHsOOg+Wh4GpqWdGyi6nUFE/JeyQvp2jsd2zlxZTQZWc+Zs+ifAPODvlBj9PcAe4KW0qwe4M+3qp4TiJju9wE3q45RwyLFWlaI89P4K8EiO8XbKs6TKOFJ/mt7hWP4feyAivt5uXc5X1Fn5fGIO5ctzZUQ81269Kucek3HpWal0GtvUN1H+42VDddaV8aLOsCuVSqVDqO8SqVQqlQ6hOuxKpVLpEKrDrlQqlQ6hOuxKpVLpEKrDrlQqlQ7hf7bBsWDDoNoJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(train[['Survived', 'SibSp', 'Parch', 'Age', 'Fare']].corr(), annot=True, fmt='.2f', cmap='coolwarm')\n",
    "# 似乎只有Fare 与生存率相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d2598fc198>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 探索生存 与 sibsp的关系 \n",
    "sns.factorplot(x='SibSp', y='Survived', data=train, kind='bar', size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d259b4af98>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived 与 Parch 的关系 \n",
    "sns.factorplot(x='Parch', y='Survived', data=train, kind='bar', size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 家庭人数少的生存机会较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age 与 Survived\n",
    "g = sns.FacetGrid(train, col='Survived')\n",
    "g = g.map(sns.distplot, 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uSbnbxo1Qwhq185ZHkH7ah8v7uK/toKCs+g1Ibdn1bLURwIgRdm6j//3PIyEpVek4kxB+AaJFJEpE/IAJQOH+GUuwjcYAVwLLjTFGROoDnwEPGWN+OFNYRHxEJMzx3BcYCWwu36Uoj8jNhS1bSqwumvNJI1o2Pk375p6bNyKpyzAC920hcK/9r9a+PTRvDm+95bGQlKpUSkwIjjaBqcCXwDZggTFmi4g8KSKjHMXeAEJFJB6YDpzpmjoVaA08Wqh7qT/wpYhsBNYDB4HXXHlhyk127oSsrPMmhF931iFuZz1G9j6GJ1uKkjsPxojX2bsEERg+HFavhh07PBeXUpWFU+MQjDFLjTFtjDGtjDHPOLY9ZoxZ4nieaYwZb4xpbYzpaYzZ49j+tDEmsEDX0lhjTJIxJsMY080Yc4ExpoMxZpoxxv0Vy6r8zjQon6fL6ZxPG+Pvm8+Q7hU/EO18cuqGcDy6p00IxjZxDRliex29/XYJOytVA+hIZVU+mzbZ9ZOLmbLiVKYXH3wTwYDOqdSp5fmcn9R1GLUO7abuzrWAbQvv0QPeecdO2KpUTaYJQZXP1q0QGWnnMSrCpz+GcvK0D8N6prg5sKId7TSQfG/fcxqXhw+3ay4vW+bBwJSqBDQhqPLZssW2zBbjg2UNCAvKpnOryjG9aG7teqS060P48vl22lXsyOXAQJg3r4SdlarmNCGossvKgt27i00IKSd8WLomhEu6pLp0eczySuoynICjCQRtWQ2Anx/07QuLFtlLUqqmqkS/pqrK2bHDfssuJiF8uDKcnFwvBnX1bGNyYcc6DiDPN+CcaqOBAyE1Fb76yoOBKeVhmhBU2W3dan+2aFHk2x8si6BZRCZtIivXyvZ5/rU5FtOf8G//h+TZObC7dYN69WD+fA8Hp5QHaUJQZbd1K3h52UblQhKS/fluQ30Gdj3u0bEHxUnqOgy/1CTqr/8WsB2l+veHxYvt6GWlaiJNCKrstm6FJk1sJXwh//s2HGOk0lUXnZHSri+5AYHnVBtdcoldWnPpUg8GppQHaUJQZXeeHkaLvg+jZePTRIZnuzko5+T7BXC048WErfwQybYtybGxEBys1Uaq5tKEoMomOxvi44tMCEnHfVm1OYh+HSv3NKJHul2Kb3oqoT99BtgRy337wuefa28jVTNpQlBlEx9vJ7YrIiEsWR2GMUL/Cyp3QkiN7klWUDgNv3rn7LZ+/Wy10fLlHgxMKQ/RhKDK5jw9jBZ9H0ajkCxaNa7crbPG24cjXUcQ8tNn+KYdBaBLF6hdGz7+2MPBKeUBmhBU2WzZYqcLbdr0nM0nMrz5Zm0wfTulVcreRYUd6T4Sr7zcs43Lfn52bqOPPz47kFmpGkMTgiqbrVuhcWO7DmUBn68JITvXi/6dKnd10RkZjaM52aQdDb48t9ooKQnWrPFgYEp5gCYEVTbF9DBatCqc4Do5dIjK8EBQZXOk+2XU2/ELtfdvA6B3b9vAvHixhwNTys00IajSy821C+MUSgi5ecIXP4fQK+ZEpZq7qCRJXYdjxIsGX70LQJ06tgvqokUeDkwpN6tCv7aq0ti9G3Jy/pAQVm+uR1qGD71jTngosLLJrhdGSrs+NPj6vbMNB3372py3fbuHg1PKjTQhqNIrpofR0jWh+Hjn063NSffHVE5Huo8kIPkA9Td8B9iEAFptpGoWTQiq9M4khEKrpH32UwidojKoU6vqdc852vFicgPq0MAxJiEiAtq21WojVbNoQlClt3UrNGwItWqd3XQgyZ/Ne+vQq4pVF52R7xdAcufBhH+3EK/TtkG8b1/b0ygx0cPBKeUmmhBU6W3e/If2g8/XhADQq33VTAgAh3uMxOd0OuGr7G1Bv352+5IlHgxKKTfShKBKJy/PLoxTKCEsXRNKw5AsmjeoupMApUV14XRYUxp99jpgm0iaNNF2BFVzaEJQpbN3r535rUBCyMoWvlkbTM/2J6vE6ORieXmR2GsM9Td8R60DOxGBCy+EZcvgRNW98VHKaU4lBBEZLiI7RCReRGYU8b6/iMx3vL9GRFo4tg8RkbUissnxc2CBfbo5tseLyIsiVfpPSc1RRA+jHzYHkZHpTa92Vf+v5uGeo8j39qHRZ68BttooOxu++MLDgSnlBiUmBBHxBmYDI4AY4GoRiSlU7CbguDGmNfACMNOx/ShwuTGmEzAJeLfAPi8DU4Box2N4Oa5DucuZhFDgDuHrtcH4eOcT2zrdQ0G5Tna9MI7FXETDL95GcrLp0AHq19d2BFUzOHOH0BOIN8bsMcZkA/OA0YXKjAbedjxfCAwSETHGrDPGHHJs3wIEOO4mGgH1jDE/GmMM8A4wptxXoyre1q22T2Zg4NlNX8eFENP8FLUDql5306Ik9rkCv7Rkwn5YjLc39OplV1HLzfV0ZEpVLGcSQhPgQIHXCY5tRZYxxuQCaUBooTLjgHXGmCxH+YQSjqkqo61bzxl/cCzNh1931aFrFRyMVpyUtr3JDGlM4yWvANCnDxw/Dj/84OHAlKpgziSEour2TWnKiEgHbDXSraU45pl9p4hInIjEJScnOxGuqjD5+bBt2znVRct+DcYYoXvb6pMQ8PLmUJ9xBK9bTu392+jRA3x94ZNPPB2YUhXLmYSQABSc9D4SOFRcGRHxAYKAFMfrSGARcL0xZneB8pElHBMAY8wcY0x3Y0z38PBwJ8JVFWb/fjh16pwG5a/XBlOnVi7tmp7yXFwVILH3GPJ9/GiyaBa1a9vJ7rT7qarunEkIvwDRIhIlIn7ABKBwE9sSbKMxwJXAcmOMEZH6wGfAQ8aYszfcxphE4KSI9Hb0Lroe0F+3yq5Qg7Ixtv0gtnU63t4ejKsC5NQJIanLMBp++Tbe6WlceKFdNXTHDk9HplTFKTEhONoEpgJfAtuABcaYLSLypIiMchR7AwgVkXhgOnCma+pUoDXwqIisdzwiHO/dDrwOxAO7gc9ddVGqghTqchp/sBb7jwRUycnsnHGw31V4Z2bQ8Kt36NPHbtPeRqo683GmkDFmKbC00LbHCjzPBMYXsd/TwNPFHDMO6FiaYJWHbd0KYWFQty4AX8cFA1Sv9oMCTjbrwInmnWjy0UscHHMn0dFefPIJ3H+/pyNTqmLoSGXlvC1bzulhtHxdMA2Cs2gSlu3BoCpWwkXXUPvgLkJ//JTevW1Po2PHPB2VUhVDE4JyjjH2DsHRfpCfD9+ur09s64yqPV1FCZI7DyYzuBFNF/yTvn3tdS9dWvJ+SlVFmhCUcw4cgIyMs+0Hm/cGcuyEL7Gtq2d10RnG24eEAddSf+NKuub+TFiYtiOo6ksTgnJOoQblFevqA9AluupPV1GSxF5jyK1Vl+YL/0nv3vDll3Z+P6WqG00IyjmFupyuWB9M49AsGgTneDAo98gLCORQn3GEr1zIgLaJnDwJ333n6aiUcj1NCMo5W7ZASAgEBZGXB99tCKJzNZjMzlkJF12D8fJh7Jan8ffXUcuqetKEoJxTYA6jDbvrkJruS5calBCyg8JJ7DWGqG9eo0en0yxZYtvZlapONCGokp3pYXSm/WC9bT+oDtNdl8ZvgyaDMYzKW8Rvv8HGjR4OSCkX04SgSnbokF0yzNF+8O26+kSGZxJev/q3HxSUFdyII91HMnHTDEQMH3/s6YiUci1NCKpkBXoY5eYJKzfWr3F3B2f8NugGGuYfonvIHhYt8nQ0SrmWJgRVsi1b7M/mzVm3qw4nTvnUqPaDgk6HN+Nwj8u56virbNgAe/Z4OiKlXEcTgirZ1q12Hcng4LPjD2rqHQLAvmFTGCO2vkjvElR1oglBlazAlBUr1teneYPThNSruetJZgU3wv/C7nRmPYs+OO3pcJRyGU0I6vwKzGGUkyt8X4PbDwraP+QmRnt/yupf/TlyxNPRKOUamhDU+R0+bBcUbt6cuB11ycj0rrHtBwXl1A2lVy8weLH4+V2eDkcpl9CEoM6vQA+jbx3jD2rSCOXzCRw1iJZee1n46jE7DapSVZwmBHV+BRLCinX1iWp0mvp18jwbUyVh/GsxqH0iy092J/mleZ4OR6ly04Sgzm/rVqhXj+w6IfywOUjbDwrpOTyEPHz48LENcLJ6TwWuqj9NCOr8HA3KP+8I4lSWth8U1qpJJi2C05h/Yjj8/e+eDkepctGEoIpnjB2U1rw5y3+tj4ihcytNCAWJwICep/mOAST+8wPYu9fTISlVZpoQVPGSk+0Cws2bs3xdMNFNTlMvUNsPCruky3EMXizMvwIeeMDT4ShVZpoQVPEcDcqnGrfmxy31asTqaGXRvEEWLRufZn7IbbBwIXz9tadDUqpMNCGo4jnmMFp9ugvZuV50idZG0+JcEnucH5LasK9hb7j9djitI5hV1eNUQhCR4SKyQ0TiRWRGEe/7i8h8x/trRKSFY3uoiKwQkXQRmVVon28dx1zveES44oKUC23aBPXqsXxPC3y887mgZYanI6q0Bnc7DsC7sc/D7t3w9NMejkip0isxIYiINzAbGAHEAFeLSEyhYjcBx40xrYEXgJmO7ZnAo8B9xRz+WmNMrOORVJYLUBVo40aIimLZr8G0b3aKWv46+Ko4DUNy6Bp9kv/b0BUzdBg89xxs3uzpsJQqFWfuEHoC8caYPcaYbGAeMLpQmdHA247nC4FBIiLGmAxjzCpsYlBVSX4+bNxIWrNOxO2sS6y2H5RoWI8U9iTWYtWARyAwEG69VUcwqyrFmYTQBDhQ4HWCY1uRZYwxuUAaEOrEsd9yVBc9KiLiRHnlLnv3QkYGK30uIT9f6KoJoUT9L0ijtn8eb//QGm67DVavhjlzPB2WUk5zJiEU9Ye68PLizpQp7FpjTCegv+NxXZEnF5kiInEiEpecnFxisMpFHAsGL0/rjr9vPjEttP2gJLX88xkQm8qCFeFkXDQCunaFGTMgMdHToSnlFGcSQgLQtMDrSOBQcWVExAcIAlLOd1BjzEHHz5PAB9iqqaLKzTHGdDfGdA8PD3ciXOUSGzeCCMv3tqBjVDp+PiXldwW22ujkaR8+/D4C7rnH9ja6+25Ph6WUU5xJCL8A0SISJSJ+wARgSaEyS4BJjudXAsuNMcX+BRERHxEJczz3BUYC2gJXmWzYQFKjzmzcq+MPSuOClhk0i8jkv4ubQGQkXH+9HZugS6upKqDEhOBoE5gKfAlsAxYYY7aIyJMiMspR7A0gVETigenA2a6pIrIP+BcwWUQSHD2U/IEvRWQjsB44CLzmustS5bZhA98G2b4DmhCcJwKj+x5lzbZ6/LK9Llx1FURHwx132HUllKrEnBqHYIxZaoxpY4xpZYx5xrHtMWPMEsfzTGPMeGNMa2NMT2PMngL7tjDGhBhj6hhjIo0xWx29j7oZYy4wxnQwxkwzxuicCJVFejrs2cPy/IsJDMilbeQpT0dUpQzrmUJt/zxmLWoCPj5w3312GpD77/d0aEqdl45UVn/k6D+//GgnOrfKwNvbw/FUMYEB+QztkcK85REkp/pCmzbwpz/BG2/AsmWeDk+pYmlCUH+0YQMHiGTXsVCtLiqjsf2Okp3rxWufNrIbJk2ybQq33AIZ2mNLVU6aENQfbdzIcr8RADp/URk1a5BFtzYnmP1xEzKzvcDf31Yd7d0Ljz3m6fCUKpImBPVH69axPHAk9evkENVQB5mX1TWDkzh0zJ83lza0Gzp3hlGj4N//hjVrPBucUkXQhKDOlZuLWbee5ZkXEts6HS/9H1JmXVqn0ykqnb+934ysbMfYzSlTICwMbroJsrM9G6BSheivuzrXtm3szGxKwukwXS6znETguqFHOHg0gP/7wnGXEBgIf/6znVr82Wc9G6BShWhCUOdau5alXApAz/baflBe3dueJKZ5Bn97vznZOY67hD59YOBAO0W2Y80JpSoDTQjqXGvXstTrclo0PE3DEK3SKC8RmDTsML8lBdjRy2fcdRfUrg033gh5OgRHVQ6aENQ5Tq7ZynemP73an/B0KNVGj3Yn6dHuBI+/1YIjKb52Y/36MHUq/PwzzJp1/gMo5SaaENTvcnNZtiGMHONL7xitLnIVEZg69iCnsrx46LWWv78xaBD07g0PP2y7oyrlYZoQ1O+2beOz7MHU8c2kY5Q2KLuxQNTbAAAe20lEQVRSs4gsxl2UzFtfNGLN1rp2o4idERXsXEfFzweplFtoQlBnmV/iWMql9Gx1DB+drsLlrh96hLCgbG76RztOZzl+9SIi4IYb4Isv4MMPPRugqvE0IaizNn51mEM0oUfnHE+HUi3VDsjn/gkH2LIvkAdeLVB1NHasnRF12jQ4qVV1ynM0IaizPv0hGICeMVpdVFF6tjvJ+IuTmLUokk9WO1aZ9fa2YxMSE+Hxxz0boKrRNCEoKyuLhQd707VePCH1cj0dTbV282WJRDc5xQ0z27H7YIDdGBMDI0fCiy/Chg2eDVDVWJoQFAA7P97KehPL4HYHPB1KtefnY3hs0j5y84ThD1xgp8gGOxNq3bpw222Qn+/ZIFWNpAlBAbDgLTslc5+LfD0cSc0QGZ7N0zft4UByAJc/3IlTmV6/J4OffrJrJyjlZpoQFADzVzflQp+fqd+kjqdDqTE6Rp3ikYn7+Xl7XS6b0YmTp7xh6FA7K+qDD0JSkqdDVDWMJgTF1i2GzSebc1njdZ4Opcbpf0EaD1+7n+831WfwvZ1JOelrxyacPAkPPODp8FQNowlBseCVFIR8+ndO83QoNdLgbqn8dfJe1sXXod9dXdjt09Yuufn227BypafDUzWIJoQazhiYv9Cbi1iJf0wrT4dTY/XrdIKZU/Zw8Kg/PW/vxvJO06BRI7j9dl03QbmNJoRKyBiYPx8uvtg+hg+Hf/0LsrJcf66VK2H74fpc67eQUxFRrj+BclqX6HT+e89O6gXmMvSRHvy3x1uYrVvtCmtKuYEmhEpm504YPBgmTLDznaWmwq5dcO+90K4dLFrk2vO99BKEeKUyvOUOdHk0z2sSls2sabvo2e4Edy4Zxu0NF5H917/B/v2eDk3VAE79BRCR4SKyQ0TiRWRGEe/7i8h8x/trRKSFY3uoiKwQkXQRmVVon24issmxz4siIq64oKrs8GG45BKIi7MDV9980345fOMNeO45+/f6iitct9DWgQPw8ceGm/NfJadVe9ccVJVbYEA+T920l2sGHeHVw2MYmrmEo7c/6umwVA1QYkIQEW9gNjACiAGuFpGYQsVuAo4bY1oDLwAzHdszgUeB+4o49MvAFCDa8RhelguoLrKzYfx4SEmx1UOjR9sZDc7o0QNefdXOmPzQQ7ZXYnknx3zlFTD5htt5mdTW3cp3MOVS3l5wy8hEHp64n5+kDz0+f4KNL33n6bBUNefMHUJPIN4Ys8cYkw3MA0YXKjMaeNvxfCEwSETEGJNhjFmFTQxniUgjoJ4x5kdjjAHeAcaU50KquunTYdUquP9+aFVM266Pj506f9Qoe8dw771lTwqZmTBnDgyJ2ECTWsc52bRD2YNXFWZIt+P8Z+ousrxq0XdaN5YvzSx5J6XKyJmE0AQoOJ9BgmNbkWWMMblAGhBawjETSjhmjbFyJcyebe8QBg48f1kvL1udNHYsvPAC/O1vZTvnG2/A0aMwLfM5UqN7YLx9ynYgVeHaRmXz1nXLaWH2cukobxYv9nREqrpyJiEUVbdf+HupM2XKVF5EpohInIjEJScnn+eQVZMxtvonPBxuusm5fUTs6otDhsBf/gIvv1y6cyYn2/16xGQwPG0eKW37lD5w5Vb+sTEsiP0bsXm/Mm6cYd48T0ekqiNnEkIC0LTA60jgUHFlRMQHCAJSSjhmZAnHBMAYM8cY090Y0z08PNyJcKuWxYvt1DWTJoG/v/P7eXnZgax9+sCdd1KqPxAzZkB6OjwR+xECHG+nCaEqSLniZj6vNZbetTZw3XWGTz7xdESqunEmIfwCRItIlIj4AROAJYXKLAEmOZ5fCSx3tA0UyRiTCJwUkd6O3kXXAzXuRjg317YJNG9uxxqUlo+PnT7/ggvguuvg889L3uenn2zvpSuvhJ6753EqojmZIY1Lf3Lldjl1Q0m+bDJL0y+ifUQK48fD8uWejkpVJyUmBEebwFTgS2AbsMAYs0VEnhSRUY5ibwChIhIPTAfOdk0VkX3Av4DJIpJQoIfS7cDrQDywG3Diz1n18v77sG0b3HjjuT2KSsPfH55+GqKiYNw4WLiw+LJ798LVV9vqqesnZFF//bektOldthMrjzjUZxw0bcYXp/rTpFEeY8bAli2ejkpVF06NQzDGLDXGtDHGtDLGPOPY9pgxZonjeaYxZrwxprUxpqcxZk+BfVsYY0KMMXWMMZHGmK2O7XHGmI6OY0493x1FdWSMbRRu2RL69y/fserUgZkz7bHGj7dVSbmF1rjZvt2eJyUFnnwSGu1ZjXfWKY5r+0HV4uXNzvEP0yhtO/PaPYGfn11Xpxo2rykP0KGpHrJ6tV0Ya8wY20hcXsHBNsGMGgX/+Ie9Y7j3Xls9NGmSbWs4dcqOcWjXDsJWfUyerz+p0T3Kf3LlVulNYzjU9090//JpXrhhI4mJttdZRUxtomoW7WvoIbNm2W/2gwe77pi+jpmTe/Sw7QkvvQQ5ORAUBF272qqpyEggP5/wb/9HSru+5PnXdl0Aym32XHYXoVtWMm7+n9h970ae+Jsf991nP3OlykoTggckJtq6/jFjoFYt1x+/Xz/7SE+3Yw2aNTt3mqKgzT/gn5JI8mV3u/7kyi3yAgLZcdVjdH7ldm7c8yhbx89k1iz7uV91laejU1WVVhl5wGuv2Tr+0YXHe7tYnTrQosUf56wL/3YBeb7+HOtwUcUGoCrU8ba9OdT7CpoueJ57L/qFjh3h5ptte5FSZaEJwc3y8uyUET16OKpvPBBA+HcLSWnfT6uLqoHdo/5MVr0IOj4/mccfysLHx/Yk0/YEVRaaENxsxQo4eBAuvdQz5w/atAr/lMMkxQ71TADKpfJq1WXnn/5C4P6t9Fj6JA88AOvXw6M6OaoqA00IbvbOO7Yq58ILPXP+BsvnkucbwLGYcvZ1VZVGSvu+JPYcTbO5MxkaEseoUfD88/bLh1KloQnBjdLT4cMPYcAA8PNz//m909No8PV7JMcOId+/AlqzlcfsHnMvWfXCaP/k1UyddJKmTe3o9ePHPR2Zqko0IbjRokV2LMCwYZ45f8Ov3sE7M4OE/hM8E4CqMLm16rJt4jPUStxDpzl38fDDcOQI3HZb+dfNUDWHJgQ3eucdaNwYOnb0wMnz82myaBZpzS8gvWnh9Y1UdZDWqhv7h95Cwy/f5qLf3mPyZFiwAN5919ORqapCE4KbHDwIy5bZgWieWCw0+Ndl1E7YycH+2km9Ots/5GZSW3WjzT9v5aZem+jc2U6VvmdPyfsqpQnBTebPt7fuQ4Z45vxNPnyR7LqhJHd24dBoVekYbx+2Xv8suQGBdH58LH+5Ow1jbHtC4fmtlCpME4KbzJ0Lbdp4ZuxB3a1rCPvpUw72HY/x8UBrtnKr7HphbJn0HP5H9nPJnGuYdlc+q1fD3//u6chUZacJwQ327IG4OLjkEg+c3BhavfoA2XVDSRgw0QMBKE84ERVL/Nj7CV2zlNt23MPgwfDEE3Y9DKWKownBDebPtz8vvtj95w796TPqb1zJvqFTyAsIdH8AymMO9f0TBwZcS+RHLzIz6hXCw2HiRDh50tORqcpKE4IbzJ8PHTpAw4buPa/k5dJyzgxOhTcnsc9Y955cVQq7R91DcqeBdHn9Dp4b+g1798Kf/+zpqFRlpQmhgm3fbtc98ER1UbP3niFw3xb2XD4N4+3r/gCU53l5s23iM6S17MrE94ZzY7+dvPmmHSCpVGGaECrY/Pm2m+mAAe49b72tP9Hinac43P0yjnbyROOFqizy/QLYdPN/ONk0hlk/dKFDk1RuuQUOHPB0ZKqy0YRQgYyBefOgc2cIC3Pfeb1PnaT9MxPJqt+AXVc86L4Tq0orLyCQjVNmkdU4ig8T+5CdkcOECXYBJaXO0IRQgTZtslVG7qwukpxsOjx+JQGJe9l2zVPk1arrvpOrSi23dj023PEKDaICeS37elavhkce8XRUqjLRhFCB5s0Db2+4yF3r0OTn0+7ZyYTEfcWOqx4lrVVXN51YVRV5AXXYOGUWQzoc4nb+yz/+AUs+zvN0WKqS0IRQQYyxg9G6doX69Sv+fJKbQ9t/3EyD5XPZPfJuDvcaU/EnVVVSvl8Am2/8F/f0/4WurOW68Zns+EmnRVWaECpMXBzs2+ee6iLvjBN0mnEZjb54i31Dp3Bg4OSKP6mq2ry8OXjFNJ6/7Fv8czMY3f8YacviPB2V8jCnEoKIDBeRHSISLyIzinjfX0TmO95fIyItCrz3kGP7DhEZVmD7PhHZJCLrRaTa/U+cNw98fKB/Ba9DE7h7I13v6E399SvYPuFx9o243TOz56kqSQYP4unxG4jPbcHEIYfJe+JpbWmuwUpMCCLiDcwGRgAxwNUiUnj+5JuA48aY1sALwEzHvjHABKADMBz4r+N4Z1xijIk1xnQv95VUIvn5trtpz552dbSKOkmTRbPodntPfFKT2XjrbK0mUmXS5sJwpl2+l0/NSKb9tT6mdx/YssXTYSkPcOYOoScQb4zZY4zJBuYBowuVGQ287Xi+EBgkIuLYPs8Yk2WM2QvEO45Xra1aZae7rqjqooBDe+h872CiX7yL4627E3fffFKjq/0/q6pAlw/M4E8XJzGbqfxj62W28eu55yBPG5xrEmcSQhOg4BCWBMe2IssYY3KBNCC0hH0N8JWIrBWRKaUPvfJ67z2oVQv69nXxgR13BT1u7ETd7b+w46pH2XTLS+TUDXHxiVRNdOvlh7iky3EezHyCD1r+BR58EHr3hl9/9XRoyk2cSQhFVUgXXpSvuDLn27evMaYrtirqThEpsnOmiEwRkTgRiUtOTnYiXM/KzLSrVPXvb5OCqwQk7qXz9EFEv3gXaVGx/PLA/0jsfYW2FyiX8fKCGdf8Rmzrk0yOf4TlE9+0U/X26AHTpsGJE54OUVUwZxJCAtC0wOtI4FBxZUTEBwgCUs63rzHmzM8kYBHFVCUZY+YYY7obY7qHh4c7Ea5nffYZpKW5cCEcY2i05FW633QBdXf8wvarHrMjToPdPFOeqhH8fAxP3biPyPAsxn50HZue+Aguvxxeegnat7eTIOkizdWWMwnhFyBaRKJExA/bSLykUJklwCTH8yuB5cYY49g+wdELKQqIBn4WkUARqQsgIoHAUGBz+S/H8957D0JDoUuX8h/LN+0onWZcRtsXbuNkZAy/3L+Aw73H6l2BqlB1auXx7C178PPNZ9jjvYkf/xDMng2BgXDllTBypO1TraqdEhOCo01gKvAlsA1YYIzZIiJPisgoR7E3gFARiQemAzMc+24BFgBbgS+AO40xeUADYJWIbAB+Bj4zxnzh2ktzv5QUe4cwcKAdoVwedbeuodstXQn+9Rt2jpvBhttfISuksWsCVaoEEcE5PHfrHk5nezHwns7sC+4CL78Md9wBK1ZATAzMnKldVKsZMVXo9q979+4mLq7yDll45RW4/XaYMweio8t+nIiv36fdczeSVS+cLZOfI71p4V6+SrlH/MFaTJ/dirCgHFa+uJ7I8CxISoJZs+D7721iePVV6NfP06Gq8xCRtc5079eRyi70+uvQqhW0bl3GAxhD83efJuZvEznRvBNrp7+vyUB5VOsmp3nutt0kpfoxaHpnDqf4QUQEPPkkPPMMHDtme1DcfLN9rqo0TQgusnatfVx2WRmr+PPzaf3SNKLefJTD3S9jw23/JTcwyOVxKlVa7Zqd5u+37OFAsj+DpncmOdWx2NKFF8Jbb8FVV8H//R+0awfvvKONzlWYJgQXee018PcvY++ivDza/OtWIhe9xIEB17L9mqcwPn4uj1GpsurUMoO/3byX3YdqMWh6Z46mOZJCrVpw2222nrRBA5g0yTaibd/u2YBVmWhCcIH0dHj/fTsyudRTVeTn0/afU2j82evsH3Izu0ffq72IVKUU2zqdZ27aw86E2gy8p8CdAkDLlvDii3DvvfZW+YIL4LHH4PRpzwWsSk0TggvMm2eTwsiRpdzRGFrPvodGn7/JvqG3sPfSOzUZqEqtW9vfk8I51UdgR7aNHAlvvw0XXwxPPQWdOsFXX3ksXlU6mhDKyRjbu6hlS9vhojRavPUYkR+9yIGLJ7Jv+O0VE6BSLtatbTrP3FxMUgAIDoaHH4bnn4esLBg2DMaPh99+80zAymmaEMpp5Up7hzx6dOm+3Dde/DIt3n2aQ73HsnvUdL0zUFVKtzY2KexKqPXH6qOzhbrBG2/ADTfAJ5/YRuennrLzu6hKSRNCOT33nP1CNGxYyWXPCF21mOgXp3K0w0XsuvJhTQaqSurWJp1nbtlL/EGbFJKOF5EU/Pzg+uttL6RevWy7QkwMLFqkvZEqIU0I5bBpEyxdCldcYXsYOaPelh+JeWoCJyNj2Hrdsxhvn4oNUqkK1DX696QwaHoxSQGgYUN4/HH45z/t6yuusIPZVq1yX7CqRJoQyuEf/7C97kaNKrksQK3fdtDpoZFkBUWw6Zb/kO/vwulQlfKQgklh4PTY4pMC2HUWXnvN9kbaudMOahs1CjZXi6nMqjxNCGW0fz/MnWsHotWrV3J5v2OJXPDAcAzCximzyKmjaxio6qNrdDp/u2Uvuw8GlJwUvL1tb6R337UjnJcvt91UJ06EHTvcF7T6A00IZfSXv9heduPHl1zWOz2NCx4cgV9qEptu/g+ZYU1L3kmpKqZLdDp/n7KXeEdSSDxWwuDKgAC49lo7iOdPf7JTa8fEwHXX2bsH5XaaEMogLs5Oc33llXZal/Pxys6k019GU3vfVjZPfp6TzTq4J0ilPCC2dTrPTtnLnsQA+t/dhb2JASXvFBRkRzt/8IH9pfrf/+zaC5Mmwa5dFR+0OksTQikZY6s/g4PhmmtKKJyXR/unr6H+hu/Yfs0THG/Xxy0xKuVJsa3Tef723SSn+tJ3ahc27w10bsfgYDtd8AcfwLhxMH++TQyTJ8Pu3RUas7I0IZTS4sV27MHkyXa9kGIZQ5t/30H494vYNfZ+krqOcFeISnlcTPNT/HtqPDl5wkV3x7Jma13ndw4JsesufPABjB1rpwJo2xZuvNEu6akqjCaEUkhJgalTISrKNiYXyxiiXn+Exp/OYf/gGzl4UUm3EkpVP1GNMnnxrnhqBeQz6N5YvlkbXLoDhITAnXfaNoYxY+zPNm3gpptg796KCbqG04TgJGNsNeeRIzBjxnlWRDOGlq89RPMP/s6hPuPYe+lUt8apVGXSKDSbF6fuokFINiMe7MQbn5VhLfDQUPtN7P33bRfV996zieHmm3UpTxfThOCkd96xbV033GD/LxYpP59WL99Hs7kzOXjhlezUUchKERqUy3+mxtOldTo3P9+O6bNbkZdXhgOFhcHdd9uEcPnl9pcyOhqmTNHE4CK6hKYTfv0VBgywq6H9859F3x14ZWfS7tnJRKyYT0L/CcSPfUCTgVIF5OXBfxc34aPvwxnQOZX3H9lKk/Dssh8wOdneNSxdal/fcAM88gg0a+aagKsRZ5fQ1IRQgu3b7WBKb2946SUID/9jGb9jiXT463iCNv/A7pHTODBwkiYDpYrx5S/B/OfDSAID8njzgR1cfmE5l95MSvo9MYjYNoaHHtLEUIAmBBfYuxcuuggyMuA//4GmRYwnC/7lK9o/MxHvUyfZfvUTJHcZ6rb4lKqqfjviz5PvtGD3oVpMGHiEf98ZT4OQnPId9MgRmxg+/9wmhuuug+nToYOO/dGEUE5ffw1XX22nc3/hBWjd+tz3fU6k0PL1h2n8yatkNGrFlutncqphK7fEplR1kJ0rzF0WwfvfNCAwII+HrvmNqWMPUjsgv3wHPnzYzivz1Vd2qu2hQ21iGDq0xt65a0Ioo9On4dln7bTtUVHwxBMQGfn7+15Zp2m49E1avP1XfE8eJ6HfBPZedif5fjpRnVJl8dsRf15e3JiftgXRMCSL6eMTuGHEYcKCynnHkJZm12H4+GM4dsyux3DjjXbOpEaNXBN8FeHShCAiw4H/AN7A68aYZwu97w+8A3QDjgFXGWP2Od57CLgJyAPuNsZ86cwxi1KRCSE7236peOQROHgQhgyBe+6xs5kC+CcdoMFX7xD50Uv4HT9Casuu7LriATKatK2QeJSqaTbtCeStLxqybldd/H3zGds/mbH9jjK8Zwr1AsvSLckhOxtWrIBPP7Wzqnp52YbBceNgxAjbW6Sa3zm4LCGIiDewExgCJAC/AFcbY7YWKHMHcIEx5jYRmQCMNcZcJSIxwFygJ9AY+AY402nzvMcsiqsTwqlTsGYNfPSRTQZnvkTceit06ZhDnd0bCF77DSE/f0HQxpWIMaS06cVvQ24mtVW3av+fSClP2JsYwJLVoXy7vj6p6b74+uTTuVU6vdqf5IKW6UQ1yqRZRCYh9XIJCszFywty8+TsIydXOJHhTWq6z7mPDB9SD54idccRUhPS7Wvqk+obTqpPOKl5dcnFGx9fL7x9BB8f8PGxsxmHhNjhEMHB9nlEhO1gEhFx7qNevcr5Z8GVCaEP8FdjzDDH64cAjDF/L1DmS0eZH0XEBzgMhAMzCpY9U86x23mPWZSyJoQNG+y3/qRDuSTszmLXLsP2nd6s2+ZPTq4Xfj55DG4ez58armSY9zcEJsYTuHczXrm2S1x642iSLxjEkW6XkRkWWcLZlFKukJcPW/YGsmZbPbbur83OA7U5lVXciFDneIkhsFYedQLyqOt7muD8Y4RmHSLsdAIhOYfxI5tcfMisFUxmrWCyAuqT5h3CcYJJzQ0kLasWaaf9OHm66IWt/PwMEeEQHm5/RoQbIhp5n5MwatX646N2bfD1hfx8Owj2zKPg62bNyp5snE0IzizX1QQ4UOB1AtCruDLGmFwRSQNCHdt/KrRvE8fzko7pMhMm2O6j9nJ9iOQA0exiOj/Tn+/pl7uKoN0nyEuoRU5IQ7LCGpM07DpONW/PyfY9yK1v+5r6Oh5KKffoG5FL314pQAp5+ZCc4sPBZD+OpPiQfsqbk6e8MAZ8vMHby+DlBd7ehjq186lbO8/xsM/rBOZT2z8frz8Mxw0DwvBJO0rt/duovW8bAQkr8Dt+BL/jSfgmHT775fCMLPw4ShhJRJzzSM4OJ+lghH0QwXYiOBLQnMzM8t82nD5tZwyvSM4khKKupPBtRXFlitte1AjpIm9VRGQKMMXxMl1Eyr2CRoLjsQKYWfCNrNOQuNc+Nv1Q3tOUJAw4WtEnqQRqwnXWhGsEvc4CsoFDjkcJMl0QEb+3Z5ZRc2cKOZMQEoCCPfAj+eO/wpkyCY4qoyAgpYR9SzomAMaYOcAcJ+KsUkQkzplbuKquJlxnTbhG0OusCZyZy+gXIFpEokTED5gALClUZgkwyfH8SmC5sY0TS4AJIuIvIlFANPCzk8dUSinlRiXeITjaBKYCX2K7iL5pjNkiIk8CccaYJcAbwLsiEo+9M5jg2HeLiCwAtgK5wJ3GmDyAoo7p+stTSinlrCo1MK06EZEpjuqwaq0mXGdNuEbQ66wJNCEopZQCdD0EpZRSDpoQ3ExEhovIDhGJF5EZno7HVUSkqYisEJFtIrJFRKY5toeIyNcissvxs5TrKFZOIuItIutE5FPH6ygRWeO4zvmOzhJVmojUF5GFIrLd8bn2qW6fp4jc4/j/ullE5opIQHX8LJ2lCcGNHNOAzAZGADHA1Y7pPaqDXOBeY0x7oDdwp+PaZgDLjDHRwDLH6+pgGrCtwOuZwAuO6zyOnb+rqvsP8IUxph3QGXu91ebzFJEmwN1Ad2NMR2wHlwlUz8/SKZoQ3KsnEG+M2WOMyQbmAaM9HJNLGGMSjTG/Op6fxP7xaIK9vrcdxd4GxngmQtcRkUjgMuB1x2sBBgILHUWq/HWKSD3gImwPQowx2caYVKrf5+kD1HKMn6oNJFLNPsvS0ITgXkVNA9KkmLJVloi0ALoAa4AGxphEsEkDiPBcZC7zb+AB4MzE/aFAqjEm1/G6OnyuLYFk4C1H1djrIhJINfo8jTEHgeeB37CJIA1YS/X7LJ2mCcG9nJkGpEoTkTrAh8CfjTEnPB2Pq4nISCDJGLO24OYiilb1z9UH6Aq8bIzpAmRQhauHiuJo/xgNRGFnYw7EVucWVtU/S6dpQnAvZ6YBqbJExBebDN43xnzk2HxERBo53m8EJHkqPhfpC4wSkX3YKr+B2DuG+o5qB6gen2sCkGCMWeN4vRCbIKrT5zkY2GuMSTbG5AAfARdS/T5Lp2lCcK9qO2WHox79DWCbMeZfBd4qOK3JJGCxu2NzJWPMQ8aYSGNMC+znt9wYcy12rsQrHcWqw3UeBg6IyJkVoAZhZxyoTp/nb0BvEant+P975hqr1WdZGjowzc1E5FLsN8ozU3Y84+GQXEJE+gHfA5v4vW79YWw7wgKgGfYXcLwxJsUjQbqYiFwM3GeMGSkiLbF3DCHAOmCiMSbLk/GVl4jEYhvO/YA9wA3YL5HV5vMUkSeAq7C95NYBN2PbDKrVZ+ksTQhKKaUArTJSSinloAlBKaUUoAlBKaWUgyYEpZRSgCYEpZRSDpoQlFJKAZoQlFJKOWhCUEopBcD/A/VJbafCtwtaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.kdeplot(train['Age'][(train['Survived']==0) & (train['Age'].notnull())], color='Red', shade=True)\n",
    "g = sns.kdeplot(train['Age'][(train['Survived']==1) & (train['Age'].notnull())], color='Blue', shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fare \n",
    "# df[df['Fare'].isnull()]\n",
    "df['Fare'].fillna(df['Fare'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age\n",
    "g = sns.kdeplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d25a071630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare'] = df['Fare'].map(lambda i : np.log(i) if i > 1 else 0)\n",
    "sns.kdeplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 分类值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d25a0f8a90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sex\n",
    "sns.barplot(x='Sex', y='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Sex'])['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d25a0dde80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass \n",
    "sns.factorplot(x='Pclass', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d259b188d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVkAAAEYCAYAAAD29oUSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGRtJREFUeJzt3Xt01eW95/HPJwEEJV6QKJxghJbIVSyQ0tOLx0J7OrTHg+2c4qU62FlaVl22nFkOjU7LoMejqz069qxyRlsZ7614A7WMtVIVtOp4IRQRAqZSBA2YSho1qBRI8p0/9qYNIZBNzJPNhvdrrb2yf7/fs3/7m81anzw8+/c8P0eEAABpFOW7AAA4lBGyAJAQIQsACRGyAJAQIQsACRGyAJAQIQsACRGyAJAQIQsACfXKdwEHaurUqfHYY4/luwzgcOZ8F1BICq4n29DQkO8SACBnBReyAFBICFkASIiQBYCECFkASIiQBYCECFkASChZyNq+zfbbttfs47htz7O93vYrtiekqgUA8iVlT/YOSVP3c/zLkiqyj5mSfpqwFgDIi2QzviLit7aH7qfJWZLuisxNxl6wfaztwRHxVqqaDkZVVVWqr6/XoEGDdN111+W7HADdLJ/Tasskvdlmuy67b6+QtT1Tmd6uysvLe6S4nlJfX6/NmzfnuwwAieTzi6+O5j93eOvciJgfEZURUVlaWpq4LADoPvkM2TpJJ7XZHiJpS55qAYAk8hmyiyXNyF5l8LeS3jvcxmMBHPqSjcnavkfS5yUNtF0n6UpJvSUpIn4m6VFJX5G0XtKHkv5rqloAIF9SXl1wXifHQ9Klqd4fhx6uxEAhKrhFu3H44koMFCJCtpu8cfWpXXpdc+MASb3U3LipS+con7u6S+8LoGewdgEAJETIAkBChCwAJETIAkBChCwAJETIAkBChCwAJETIAkBChCwAJMSMrzwb2LdVUnP25+GB2XE4nBCyeTZ73Lv5LgFAQgwXAEBChCwAJETIAkBChCwAJETIAkBChCwAJETIAkBChCwAJETIAkBChCwAJETIAkBChCwAJETIAkBChCwAJMRShygYh+Pauyh8hCwKBmvvohAxXAAACRGyAJAQIQsACRGyAJAQIQsACRGyAJBQ0pC1PdV2re31tq/o4Hi57WW2V9p+xfZXUtYDAD0tWcjaLpZ0o6QvSxot6Tzbo9s1myPp/ogYL+lcSTelqgcA8iFlT3aSpPURsSEidkq6V9JZ7dqEpKOzz4+RtCVhPQDQ41KGbJmkN9ts12X3tXWVpAts10l6VNJ3OzqR7Zm2q21Xb926NUWtAJBEypB1B/ui3fZ5ku6IiCGSviLp57b3qiki5kdEZURUlpaWJigVANJIGbJ1kk5qsz1Eew8HXCTpfkmKiOcl9ZU0MGFNANCjUobsckkVtofZ7qPMF1uL27V5Q9IXJMn2KGVClvEAAIeMZCEbEc2SviNpiaR1ylxFUGP7atvTss3+u6Rv2V4l6R5J34yI9kMKAFCwki51GBGPKvOFVtt9c9s8XyvpsylrAIB8YsYXACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACREyAJAQoQsACSUNGRtT7Vda3u97Sv20eZs22tt19hekLIeAOhpvVKd2HaxpBsl/b2kOknLbS+OiLVt2lRI+h+SPhsR79g+IVU9AJAP+w1Z29skxb6OR8TR+3n5JEnrI2JD9lz3SjpL0to2bb4l6caIeCd7vrdzrBsACsJ+QzYiSiTJ9tWS6iX9XJIlnS+ppJNzl0l6s812naRPtWtzSvb8z0kqlnRVRDzW/kS2Z0qaKUnl5eWdvC0AHDxyHZP9TxFxU0Rsi4imiPippH/q5DXuYF/7XnEvSRWSPi/pPEm32D52rxdFzI+IyoioLC0tzbFkAMi/XEO2xfb5tottF9k+X1JLJ6+pk3RSm+0hkrZ00OaXEbErIl6XVKtM6ALAISHXkP2GpLMl/TH7mJ7dtz/LJVXYHma7j6RzJS1u1+ZhSZMlyfZAZYYPNuRYE4B9qKqq0owZM1RVVZXvUg57OV1dEBEblfnSKmcR0Wz7O5KWKDPeeltE1GTHd6sjYnH22Jdsr1WmZ/y9iPjTgbwPgL3V19dr8+bN+S4DyjFkbZ8i6aeSToyIsbbHSZoWEdfs73UR8aikR9vtm9vmeUi6LPsAgENOrsMF/0eZ61l3SVJEvKLMf/8BAPuR62SEIyPiJXuPCwaaE9QDoACsWLHihF69et0iaayYni9JrZLWNDc3Xzxx4sQ9rvfPNWQbbH9c2UuwbH9d0lvdWyOAQtGrV69bBg0aNKq0tPSdoqKifU5YOly0trZ669ato+vr62+RNK3tsVxD9lJJ8yWNtL1Z0uvKTEgAcHgaS8D+VVFRUZSWlr5XX18/tv2xXEN2U0R80fZRkooiYlv3lgigwBQRsHvKfh57DZ3kOpbyuu35kv5W0vvdWRgAdObyyy8fNHz48DGnnHLK6JEjR45eunTpUfmuKVe59mRHSPpHZYYNbrX9iKR7I+LZZJUBgKQnnnjiqCVLlhy7evXqtf369Yu33nqr144dOzqatn9QyqknGxHbI+L+iPjPksZLOlrS00krAwBJmzdv7j1gwIDmfv36hSQNHjy4eejQobueeeaZIz/5yU+OGDNmzKjPfe5zFZs2beq9a9cujR07dtQjjzxSIkmXXnpp2Xe/+92yfNaf86UXts+wfZOk30nqq8w0WwBI6qtf/WrTli1b+gwdOnTsBRdcUP6rX/2q/44dOzxr1qzyX/7yl3+oqalZd+GFFzbMnj27rHfv3rrjjjtenzVrVvlDDz109NKlS4+5/vrr26+Z0qNynfH1uqSXJd2vzNTXD5JWBQBZxxxzTOuaNWvWPvbYYyVPPvlkyYUXXvjxyy67bMtrr73Wb8qUKadIUmtrq0pLS3dJUmVl5Z/PPvvsP5177rnDly5duq5v3755/YIu1zHZ0yKiKWklALAPvXr10plnnrntzDPP3DZu3LjtP/vZz0qHDx++/eWXX361o/Y1NTX9SkpKWt56663ekrb3cLl72O9wge3dS/hca3te+0cP1AfgMLdq1aojVq9efcTu7ZUrV/arqKj4c2NjY68nnnjiKEnasWOHq6ur+0rSnXfeeWxjY2OvpUuXvjp79uzyhoaG4nzVLnXek12X/VmduhAA6EhTU1PxrFmzypuamoqLi4tj6NChO+68885Nr7/++tZZs2aVb9u2rbilpcWXXHLJH8vKypqvvPLKIU888UTt8OHDd1188cVvz5w586QHH3xwY77q7+z2M/83+/SViFjZA/UAwB5OP/30D1euXLnXsMDgwYObq6ura9vv37hx45rdz+fMmZP3+wbmenXBj22/avtfbY9JWhEAHEJyvU52sjL34doqab7t1bbnpCwMAA4FuV5doIiolzTP9jJJVZLmStrvot0APpo3rj61S69rbhwgqZeaGzd16Rzlc1d36X2xt5x6srZH2b7K9hpJ/1vS/1PmxogAgP3ItSd7u6R7JH0pIvI6ewIACkmnIWu7WNIfIuInPVAPABxSOh0uiIgWScdnb+sNAAXvkUceKZk8efLwnnivnBftlvSc7cWS/rJuQUT8OElVAArKxO/dNbE7z7fi+hkruvN8+ZTrdbJbJD2SbV/S5gEAeVFbW9tn2LBhY84555yTKyoqxkybNm3Yww8/XDJhwoSRJ5988thly5YduWzZsiPHjx8/ctSoUaPHjx8/ctWqVUe0P09TU1PR9OnTh44dO3bUqFGjRv/iF784tjvrzKknGxH/0p1vCgDd4c033+x73333bZg4ceKmcePGjbr77ruPr66ufnXBggXHXnvttYPvv//+11966aVXe/furYcffrikqqpqyJIlS/7Q9hzf//73B0+ePLnpgQce2NjQ0FBcWVk5atq0aU1HH310a3fUmOtSh8uUvVNtWxExpTuKAICuKCsr2zFp0qTtknTKKadsnzJlSlNRUZEmTJjw4TXXXPM3jY2Nxeecc86wjRs39rUdu3bt2uuOCk899dTRS5YsOXbevHmDpMxiM+vXr+8zYcKEP3dHjbmOyc5u87yvpH+S1NwdBQBAV/Xp0+cvnb+ioiLtXju2uLhYLS0tvvzyy8vOOOOMbY8//vgfamtr+0yZMmVE+3NEhBYuXLj+tNNO25Gixlyn1a5o83guIi6T9KkUBeVLVVWVZsyYoaqqqs4bAygITU1NxUOGDNkpSTfffPPAjtpMnjy56YYbbjixtTUzOvDcc8/1684acp3xNaDNY6DtqZIGdWch+VZfX6/Nmzervr4+36UA6CaXX355/VVXXTVkwoQJI1taWjps86Mf/WhLc3OzR44cObqiomLMnDlzuvWeYLkOF6zQX8dkmyVtlHRRdxYCoHDl45KrESNG7Hzttddqdm8vWrRoY0fH2i59+JOf/GSLpL/cZUGS+vfvHwsWLNiUqs79hqztT0p6MyKGZbcvVGY8dqOktamKAoBDRWfDBTdL2ilJtv9O0g8l3SnpPUnz05YGAIWvs+GC4ohozD4/R9L8iFgkaZHtl9OWBgCFr7OebLHt3UH8BUlL2xzLeS1aADhcdRaU90h62naDMrfVfUaSbA9XZsgAALAfnd1I8VrbT0oaLOk3EbH7CoMiSd9NXRyArhnYt1VSc/Yn8imXpQ5fiIiHIqLt6lu/j4jfdfZa21Nt19peb/uK/bT7uu2wXZl76QD2Zfa4d/WjSY2aPe7dfJeSzDXXXHPCxz72sTHTpk0bluL8l1122d/MnTv3xI96nmTjqtnFvm+U9PeS6iQtt704Ita2a1ciaZakF1PVAiCtN64+tVuXOiyfu7rT625vvfXW0l//+tevjRw5cmd3vnd3S/nl1SRJ6yNigyTZvlfSWdr7+tp/lXSd9lwfAQD26Rvf+EZ5XV3dEdOmTRv+ta99rXHDhg19161b16+lpcU/+MEPtlxwwQXvzps37/jFixcf29ra6tra2n6XXnpp/c6dO4vuu+++4/v06dP6m9/85rUTTzyx5YYbbhh4++23l+7atctDhw7dsXDhwtdLSkr2GGepqak54tvf/nZ5Y2Njr759+7becsstm8aPH5/TAjK5rifbFWWS3myzXZfd9xe2x0s6KSIe2d+JbM+0XW27euvWrd1fKYCCsmDBgjdOOOGEXU8//fTvP/jgg+LJkyc3rVmzZt0zzzxTO2fOnCFNTU1FkvT73/++36JFizYsX7583Q9/+MOyI488snXdunVrKysrP7j55puPl6Tzzz//nTVr1qyrra1dO2LEiO3z5s3ba42Diy+++OSbbrrpjZqamnXXX3993SWXXFKea60pe7J7LSmmNssl2i6S9O+SvtnZiSJivrKTHyorK/dachHA4WtfSxVK0mc+85ltxx13XOtxxx3X2r9//5bp06e/K0mnnnrqh6+88sqRkrRixYp+c+fOLdu2bVvxBx98UHzGGWfsceXUe++9V7Ry5cr+06dP//jufTt37uwo3zqUMmTrJJ3UZnuIMndY2K1E0lhJT9mWMgvOLLY9LSKqE9YF4BCyr6UKn3322aP2tRRiUVGRmpubLUkzZ84ctnDhwvWf/vSnt8+bN+/4p59+eo+7vrS0tKikpKT51Vdf7dJSAimHC5ZLqrA9LHsTxnMlLd59MCLei4iBETE0IoZKekESAQvggHzUpQo//PDDovLy8l07duzwvffeO6D98QEDBrQOGTJk52233XacJLW2tur555/P+T2ShWxENEv6jqQlktZJuj8iamxfbXtaqvcFcHj5qEsVXnHFFVsmTZo06vTTTz+loqKiwy+z7rnnng233377wBEjRoyuqKgYs2jRopzvA+a/zi8oDJWVlVFd3f2d3RkzZmjz5s0qKyvTXXfddcCvf+PqU7u9plyUz12dl/f9KPiscneQflZetWrVxtNOO62hp+opFKtWrRp42mmnDW27L+VwAQAc9g65RV4mfu/Ae6GSVNKwTcWS3mjY1qVzPMQN0gF0gJ4sACREyALoitbW1tacrxU9HGQ/j71W5CFkAXTFmq1btx5D0Ga0trZ669atx0ha0/7YITcmCyC95ubmi+vr62+pr68fKzprUqYHu6a5ufni9gcIWQAHbOLEiW9L4nr3HPAXCAASImQBICFCFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASYhWurNY+R+3xEwC6AyGb9UHFl/JdAoBDEMMFAJAQIQsACRGyAJAQIQsACRGyAJAQIQsACRGyAJAQIQsACRGyAJAQM75wwKqqqlRfX69Bgwbpuuuuy3c5wEGNkMUBq6+v1+bNm/NdBlAQGC4AgIQIWQBIKGnI2p5qu9b2ettXdHD8Mttrbb9i+0nbJ6esBwB6WrKQtV0s6UZJX5Y0WtJ5tke3a7ZSUmVEjJO0UBLfogA4pKTsyU6StD4iNkTETkn3SjqrbYOIWBYRH2Y3X5A0JGE9ANDjUoZsmaQ322zXZffty0WSfp2wHgDocSkv4XIH+6LDhvYFkiolnbGP4zMlzZSk8vLy7qoPAJJL2ZOtk3RSm+0hkra0b2T7i5J+IGlaROzo6EQRMT8iKiOisrS0NEmxAJBCypBdLqnC9jDbfSSdK2lx2wa2x0u6WZmAfTthLQCQF8lCNiKaJX1H0hJJ6yTdHxE1tq+2PS3b7HpJ/SU9YPtl24v3cToAKEhJp9VGxKOSHm23b26b519M+f4AkG+sXQAkxGI6IGSBhFhMB6xdAAAJEbIAkBDDBYexid+7q0uvK2nYpmJJbzRs69I5Hirp0tsCBYmeLAAkRMgCQEKELAAkRMgCQEJ88QXkgC8J0VX0ZAEgIUIWABIiZAEgIUIWABIiZAEgIUIWABIiZAEgIUIWABJiMgKQUGufo/b4icMPIYsDRnDk7oOKL+W7BOQZIYsDRnAAuWNMFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASImQBICFCFgASShqytqfarrW93vYVHRw/wvZ92eMv2h6ash4A6GnJQtZ2saQbJX1Z0mhJ59ke3a7ZRZLeiYjhkv5d0r+lqgcA8iFlT3aSpPURsSEidkq6V9JZ7dqcJenO7POFkr5g2wlrAoAelfL2M2WS3myzXSfpU/tqExHNtt+TdLykhraNbM+UNDO7+b7t2iQVfwQnSwPVru4ecWXh/U3is8rdQfpZPRYRU3uqlEKXMmQ7+leKLrRRRMyXNL87ikrFdnVEVOa7jkLAZ5U7PqvCl3K4oE7SSW22h0jasq82tntJOkZSY8KaAKBHpQzZ5ZIqbA+z3UfSuZIWt2uzWNKF2edfl7Q0IvbqyQJAoUo2XJAdY/2OpCWSiiXdFhE1tq+WVB0RiyXdKunnttcr04M9N1U9PeCgHs44yPBZ5Y7PqsCZjiMApMOMLwBIiJAFgIQI2Y/I9m2237a9Jt+1HMxsn2R7me11tmts/3O+azpY2e5r+yXbq7Kf1b/kuyZ0HWOyH5Htv5P0vqS7ImJsvus5WNkeLGlwRPzOdomkFZK+GhFr81zaQSc76/GoiHjfdm9Jz0r654h4Ic+loQvoyX5EEfFbcW1vpyLirYj4Xfb5NknrlJnxh3Yi4/3sZu/sg95QgSJk0eOyq62Nl/Rifis5eNkutv2ypLclPR4RfFYFipBFj7LdX9IiSf8tIpryXc/BKiJaIuITysyUnGSboagCRciix2THFxdJujsiHsx3PYUgIt6V9JQkFmQpUIQsekT2y5xbJa2LiB/nu56Dme1S28dmn/eT9EVJr+a3KnQVIfsR2b5H0vOSRtius31Rvms6SH1W0n+RNMX2y9nHV/Jd1EFqsKRltl9RZg2QxyPikTzXhC7iEi4ASIieLAAkRMgCQEKELAAkRMgCQEKELAAkRMiiS2y3ZC/DWmP7AdtH7qftVbZn92R9wMGCkEVXbY+IT2RXHtsp6dv5Lgg4GBGy6A7PSBouSbZn2H4luxbqz9s3tP0t28uzxxft7gHbnp7tFa+y/dvsvjHZdVVfzp6zokd/K6AbMBkBXWL7/Yjon72V+yJJj0n6raQHJX02IhpsD4iIRttXSXo/Iv6X7eMj4k/Zc1wj6Y8R8R+2V0uaGhGbbR8bEe/a/g9JL0TE3dk7HhdHxPa8/MJAF9GTRVf1yy7FVy3pDWXWJZgiaWFENEhSRHS0zu5Y289kQ/V8SWOy+5+TdIftbylzd2MpM135+7Yvl3QyAYtClOyW4Djkbc8uxfcX2UVgOvuv0R3K3BFhle1vSvq8JEXEt21/StI/SHrZ9iciYoHtF7P7lti+OCKWdvPvASRFTxbd6UlJZ9s+XpJsD+igTYmkt7LLHp6/e6ftj0fEixExV1KDpJNsf0zShoiYJ2mxpHHJfwOgm9GTRbeJiBrb10p62naLpJWSvtmu2f9U5o4ImyStViZ0Jen67BdbViasV0m6QtIFtndJqpd0dfJfAuhmfPEFAAkxXAAACRGyAJAQIQsACRGyAJAQIQsACRGyAJAQIQsACf1/W7ayX/j9muwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 358.5x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass and Sex \n",
    "sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embarked \n",
    "df['Embarked'] = df['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d25401c0b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked \n",
    "sns.factorplot(x='Embarked', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d259d28d68>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked , Pclass \n",
    "sns.factorplot('Pclass', col='Embarked', data=df, kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d25a2acd30>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 358.5x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age | Sex, Parch, Pclass, SibSp \n",
    "sns.factorplot(y='Age', x='Sex', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='Pclass', hue='Sex', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='SibSp', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='Parch', data=df, kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d25a37ada0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['Age', 'Sex', 'SibSp', 'Parch', 'Pclass']].corr(), cmap='BrBG', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 填充年龄缺失值 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_title = [i.split(',')[1].split('.')[0].strip() for i in df['Name']]\n",
    "df['Title'] = df_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d25b5124a8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Title', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Capt              1\n",
       "Col               4\n",
       "Don               1\n",
       "Dona              1\n",
       "Dr                8\n",
       "Jonkheer          1\n",
       "Lady              1\n",
       "Major             2\n",
       "Master           61\n",
       "Miss            260\n",
       "Mlle              2\n",
       "Mme               1\n",
       "Mr              757\n",
       "Mrs             197\n",
       "Ms                2\n",
       "Rev               8\n",
       "Sir               1\n",
       "the Countess      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Title')['Title'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(['Capt','Major','Col','Don','Dona','Dr','Jonkheer','Mlle','Mme','Ms','Rev', 'the Countess'], 'Rare', inplace=True)\n",
    "df.replace(['Sir'], 'Mr', inplace=True)\n",
    "df.replace(['Lady'], 'Miss', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in df['Cabin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d25a3ba358>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='Cabin', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d25b5de940>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(y='Survived', x='Cabin', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ticket'] = pd.Series([t.replace('/', '').replace('.','').split(' ')[0] if len(t.split(' '))-1 else 'X' for t in df.Ticket])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fsize'] = df['SibSp'] + df['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d25b516438>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Fsize', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sex'] = df['Sex'].map({'female': 0, 'male': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "index_Nan_age = list(df['Age'][df['Age'].isnull()].index)\n",
    "for i in index_Nan_age:\n",
    "    age_med = df['Age'].median()\n",
    "    age_pred = df['Age'][((df['SibSp']==df.iloc[i]['SibSp']) & \\\n",
    "                            (df['Parch']==df.iloc[i]['Parch'])&\\\n",
    "                            (df['Pclass']==df.iloc[i]['Pclass']))].median()\n",
    "    if not np.isnan(age_pred):\n",
    "        df['Age'].iloc[i] = age_pred \n",
    "    else:\n",
    "        df['Age'].iloc[i] = age_med"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 14 columns):\n",
      "Age            1309 non-null float64\n",
      "Cabin          1309 non-null object\n",
      "Embarked       1309 non-null object\n",
      "Fare           1309 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null int64\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "Title          1309 non-null object\n",
      "Fsize          1309 non-null int64\n",
      "dtypes: float64(3), int64(6), object(5)\n",
      "memory usage: 143.2+ KB\n",
      "['X' 'C' 'E' 'G' 'D' 'A' 'B' 'F' 'T']\n",
      "['S' 'C' 'Q']\n",
      "['A5' 'PC' 'STONO2' 'X' 'PP' 'CA' 'SCParis' 'SCA4' 'A4' 'SP' 'SOC' 'WC'\n",
      " 'SOTONOQ' 'WEP' 'STONO' 'C' 'SCPARIS' 'SOP' 'Fa' 'FCC' 'SWPP' 'SCOW'\n",
      " 'PPP' 'SC' 'SCAH' 'AS' 'SOPP' 'FC' 'SOTONO2' 'CASOTON' 'SCA3' 'STONOQ'\n",
      " 'AQ4' 'A' 'LP' 'AQ3']\n",
      "['Mr' 'Mrs' 'Miss' 'Master' 'Rare']\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "print(df.Cabin.unique())\n",
    "print(df.Embarked.unique())\n",
    "print(df.Ticket.unique())\n",
    "print(df.Title.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['Cabin', 'Embarked','Ticket', 'Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PassengerId', 'Name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>...</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_X</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Rare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.972177</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age      Fare  Parch  Pclass  Sex  SibSp  Survived  Fsize  Cabin_A  \\\n",
       "0  22.0  1.981001      0       3    1      1       0.0      1        0   \n",
       "1  38.0  4.266662      0       1    0      1       1.0      1        0   \n",
       "2  26.0  2.070022      0       3    0      0       1.0      0        0   \n",
       "3  35.0  3.972177      0       1    0      1       1.0      1        0   \n",
       "4  35.0  2.085672      0       3    1      0       0.0      0        0   \n",
       "\n",
       "   Cabin_B     ...      Ticket_STONOQ  Ticket_SWPP  Ticket_WC  Ticket_WEP  \\\n",
       "0        0     ...                  0            0          0           0   \n",
       "1        0     ...                  0            0          0           0   \n",
       "2        0     ...                  0            0          0           0   \n",
       "3        0     ...                  0            0          0           0   \n",
       "4        0     ...                  0            0          0           0   \n",
       "\n",
       "   Ticket_X  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Rare  \n",
       "0         0             0           0         1          0           0  \n",
       "1         0             0           0         0          1           0  \n",
       "2         0             0           1         0          0           0  \n",
       "3         1             0           0         0          1           0  \n",
       "4         1             0           0         1          0           0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "train = df[:train_len]\n",
    "test = df[train_len:]\n",
    "test.drop('Survived', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Y_train = train['Survived']\n",
    "X_train = train.drop('Survived', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DesisionTreeClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-cc2a107f6df3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mkfold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStratifiedKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mclassifiers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mclassifiers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAdaBoostClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDesisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlearn_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mclassifiers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRandomForestClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mclassifiers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGradientBoostingClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'DesisionTreeClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "random_state = 2\n",
    "kfold = StratifiedKFold(n_splits=10)\n",
    "classifiers = []\n",
    "classifiers.append(AdaBoostClassifier(DesisionTreeClassifier(random_state=random_state), random_state=random_state, learn_rate=0.1))\n",
    "classifiers.append(RandomForestClassifier(random_state=random_state))\n",
    "classifiers.append(GradientBoostingClassifier(random_state=random_state))\n",
    "\n",
    "cv_results = []\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier, X_train, y=Y_train, scoring='accuracy', cv=kfold, n_jobs=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cv_result in cv_results:\n",
    "    print(cv_result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(2,10):\n",
    "#     for j in range(2, 10):\n",
    "rf = RandomForestClassifier(random_state=2, n_estimators=500, min_samples_split=7, min_samples_leaf=3)\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=False, random_state=2)\n",
    "scores = model_selection.cross_val_score(rf, X_train, Y_train, cv=kf)\n",
    "#     print(scores)\n",
    "print(i, j, scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf_params = {\n",
    "    'max_features':[1,3,10],\n",
    "    'min_samples_split':[2,3,5,7,10],\n",
    "    'min_samples_leaf':[1,3,5,7,10],\n",
    "    'n_estimators': [100, 300],\n",
    "    'criterion': ['gini']}\n",
    "gsrf = GridSearchCV(rf, param_grid=rf_params, cv=kfold, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsrf.fit(X_train, Y_train)\n",
    "gsrf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsrf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_survived = gsrf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('submission.csv')\n",
    "submission['Survived'] = test_survived.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission_08_21.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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